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Free Download LLM Fine-Tuning GRPO, SFT, DPO, with Reinforcement Learning Published: 3/2025 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 3h 40m | Size: 1.85 GB LLM Hands-On Fine-Tuning and Reinforcement Learning with SFT, LoRA, DPO, and GRPO What you'll learn You will grasp the core principles of Large Language Models (LLMs) and the overall structure behind their training processes. You will learn the differences between base models and instruct models, as well as the methods for preparing data for each. You'll learn data preprocessing techniques along with essential tips, how to identify special tokens required by models, understanding data formats, and methods You'll gain practical, hands-on experience and detailed knowledge of how LoRA and Data Collator work. You'll gain a detailed understanding of crucial hyperparameters used in training, including their purpose and how they function. You'll practically learn, in detail, how trained LoRA matrices are merged with the base model, as well as key considerations and best practices to follow during You'll learn what Direct Preference Optimization (DPO) is, how it works, the expected data format, and the specific scenarios in which it's used. You'll learn key considerations when preparing data for DPO, as well as understanding how the DPO data collator functions. You'll learn about the specific hyperparameters used in DPO training, their roles, and how they function. You'll learn how to upload your trained model to platforms like Hugging Face and manage hyperparameters effectively after training. You'll learn in detail how Group Relative Policy Optimization (GRPO), a reinforcement learning method, works, including an in-depth understanding of its learnin You'll learn how to prepare data specifically for Group Relative Policy Optimization (GRPO). You'll learn how to create reward functions-the most critical aspect of Group Relative Policy Optimization (GRPO)-through various practical reward function exam In what format should data be provided to GRPO reward functions, and how can we process this data within the functions? You'll learn these details thoroughly. You'll learn how to define rewards within functions and establish clear reward templates for GRPO. You'll practically learn numerous details, such as extracting reward-worthy parts from raw responses and defining rewards based on these extracted segments. You'll learn how to transform an Instruct model into one capable of generating "Chain of Thought" reasoning through GRPO (Group Relative Policy Optimization). Requirements Basic knowledge of Python programming. Introductory-level familiarity with artificial intelligence and machine learning concepts. Ideally, prior experience with Jupyter Notebook or Google Colab. Description In this course, you will step into the world of Large Language Models (LLMs) and learn both fundamental and advanced end-to-end optimization methods. You'll begin with the SFT (Supervised Fine-Tuning) approach, where you'll discover how to properly prepare your data and create customized datasets using tokenizers and data collators through practical examples. During the SFT process, you'll learn the key techniques for making large models lighter and more efficient with LoRA (Low-Rank Adaptation) and quantization, and explore step by step how to integrate them into your projects.After solidifying the basics of SFT, we will move on to DPO (Direct Preference Optimization). DPO allows you to obtain user-focused results by directly reflecting user feedback in the model. You'll learn how to format your data for this method, how to design a reward mechanism, and how to share models trained on popular platforms such as Hugging Face. Additionally, you'll gain a deeper understanding of how data collators work in DPO processes, learning practical techniques for preparing and transforming datasets in various scenarios.The most significant phase of the course is GRPO (Group Relative Policy Optimization), which has been gaining popularity for producing strong results. With GRPO, you will learn methods to optimize model behavior not only at the individual level but also within communities or across different user groups. This makes it more systematic and effective for large language models to serve diverse audiences or purposes. In this course, you'll learn the fundamental principles of GRPO, and then solidify your knowledge by applying this technique with real-world datasets.Throughout the training, we will cover key topics-LoRA, quantization, SFT, DPO, and especially GRPO-together, supporting each topic with project-oriented applications. By the end of this course, you will be fully equipped to manage every stage with confidence, from end-to-end data preparation to fine-tuning and group-based policy optimization. Developing modern and competitive LLM solutions that focus on both performance and user satisfaction in your own projects will become much easier. Who this course is for Who is this course for? Data scientists and ML engineers who want to specialize in Large Language Model (LLM) training techniques. Individuals who want to master essential tips and best practices for data preparation. AI developers aiming to build their own customized language models. Individuals who want hands-on experience with advanced techniques like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO). Individuals who want practical, hands-on experience with the Group Relative Policy Optimization (GRPO) technique. Individuals who want to learn essential tips for data preparation and adapt their own custom datasets for language models. Those interested in reinforcement learning methods and optimizing models based on user feedback. 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Free Download LLM Apps - Prototyping, Model Evaluation, and Improvements Published: 3/2025 Created by: Dan Andrei Bucureanu MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Expert | Genre: eLearning | Language: English | Duration: 69 Lectures ( 5h 53m ) | Size: 3.25 GB Design, Test, and Benchmark LLM Apps: Fast Prototyping and Smart Evaluation for Optimal Performance What you'll learn Understand the tech landscape of LLM powered APPs When to use GEN AI and when to use Weak AI Setup the tools to integrate AI into your standard APP Get the basics of ai in module Introduction to AI Overview of Machine Learning Types Data Lifecycle - how data evolves with your ML Model Foundation Model lifecycle Fine Tunning of models through data Fine Tunning of models through prompt Fine Tunning of models through hyperparameter Using Huggingface models for work Agentic Frameworks as: Autogen, Browser User, Flowise AI Understand RAG and how to evaluate it Evaluate the LLM With RAGAs benchmarking Framework Understand the Confusion Matrix: accuracy, Recall, F1 score GLUE Benchmarking Framework Retrain and fine tune a computer Vision model Requirements Some AI Experience Experience with Prompting Some coding experience with Python Laptop abele to run VS code and some python apps LLM Api key 7-8 Hours and the will to improve Description Unlock the full potential of Large Language Models (LLMs) by understanding prototyping, model evaluation, and benchmarking. This hands-on course takes you through every stage of LLM development-from building and selecting models to fine-tuning, testing, and benchmarking them with industry-standard tools. Whether you're an AI beginner or a professional looking to enhance your expertise, this course provides the skills needed to create high-performing AI applications.What you'll learn: Set Up Your AI Development EnvironmentLearn how to prepare a powerful AI workspace with Python, VS Code, NPM, and essential AI libraries, ensuring a seamless development experience.Understand AI & Machine Learning BasicsExplore key concepts in AI, Machine Learning, and Deep Learning, including supervised vs. unsupervised learning, model training phases, and how LLMs process and generate responses.Selecting the Right AI Model for Your Use CaseDiscover how to choose the best pre-trained AI models for NLP, vision, and multi-modal applications. Learn when to use classification, clustering, and regression models and understand model complexity, speed, and accuracy trade-offs. Harness the Power of Retrieval-Augmented Generation (RAG)Enhance your AI applications with RAG, a technique that combines retrieval-based search with LLM responses for more accurate and context-aware AI outputs.Leverage the Hugging Face AI CommunityTap into the Hugging Face ecosystem-explore model repositories, learn about tokenizers and transformers, and contribute to the open-source AI movement.Fine-Tune Models for Maximum PerformanceExperiment with temperature settings, top-K and top-P sampling, and hyperparameter tuning to optimize LLM responses and efficiency.Supercharge Your AI with Data-Driven InsightsImprove model accuracy with K-Fold Cross Validation, learn effective data-splitting techniques, and explore overfitting and underfitting detection methods.Benchmark Your AI Models Like a ProCompare your models against industry benchmarks like GLUE and Hugging Face Leaderboards. Learn how to evaluate NLP models using standard metrics and perform real-world GLUE benchmarking with Python.Evaluate Computer Vision AI ModelsGo beyond text-based models! Learn how to benchmark vision-based AI models using CIFAR-10 and interpret test results for advanced model evaluation. Understand Model Evaluation with Confusion MatricesMaster Confusion Matrix analysis to assess classification model performance. Learn how to interpret True Positives, False Positives, False Negatives, and True Negatives to optimize AI predictions and reduce errors.Who Should Take This Course? AI enthusiasts eager to dive into LLM prototyping and evaluation Developers looking to build and refine state-of-the-art AI models Data scientists who want to benchmark AI performance with confidence Anyone interested in understanding AI model evaluation techniques Who this course is for Any Software engineer Developers AI engineers Project Managers Product Owners AI Testing Engineers Homepage: https://www.udemy.com/course/llm-apps-prototyping-model-evaluation-and-improvements/ [b]AusFile[/b] https://ausfile.com/hml8yfi4p2d3/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part1.rar.html https://ausfile.com/0x13rqgc4flt/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part2.rar.html https://ausfile.com/aqmntqz8eayj/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part3.rar.html https://ausfile.com/gipphnwkwy7z/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part4.rar.html Rapidgator https://rg.to/file/4e0c5cd502e4e0ac57ea2fc089926b5a/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part1.rar.html https://rg.to/file/44cfe4b09357cb1968b48fb028aba412/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part2.rar.html https://rg.to/file/4d8a6197462b3dcc048a7c7c82f8da60/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part3.rar.html https://rg.to/file/6518b947c286912d1c03a82619c82298/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part4.rar.html Fikper Free Download https://fikper.com/Se29llobp3/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part1.rar.html https://fikper.com/V2P57xnKBs/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part2.rar.html https://fikper.com/lPpr6xhzec/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part3.rar.html https://fikper.com/THpHrzwRBJ/rbvod.LLM.Apps.Prototyping.Model.Evaluation.and.Improvements.part4.rar.html No Password - Links are Interchangeable

Free Download Building Generative AI Projects with LLM, Langchain, GAN Published: 3/2025 Created by: Christ Raharja MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 22 Lectures ( 4h 34m ) | Size: 1.8 GB Learn how to build generative AI apps using large language models, langchain, and generative adversarial networks What you'll learn Learn the basic fundamentals of large language model and generative adversarial network, such as getting to know their use cases and understanding how they work Learn how to build legal document analyzer using LLM Learn how to analyze Excel data using LLM Learn how to build AI short story generator using LLM Learn how to build AI code generator using LLM Learn how to build customer support chatbot using LLM Learn how to build report summarizer using LLM Learn how to build AI travel planner using Langchain Learn how to build AI math solver using Langchain Learn how to build AI random face generator using ProGAN Learn how to build random digital art generator using Deep Convolutional GAN Learn how to build generator and discriminator functions Learn how to train and fine tune GAN model Learn how to create user interface using Streamlit and deploy app to Hugging Face Space Learn how to build LLM based apps using Dify AI and Relevance AI Learn how to find AI models in Hugging Face and download dataset from Kaggle Requirements No previous experience in LLM is required Basic knowledge in Python Description Welcome to Building AI Projects with LLM, Langchain, GAN course. This is a comprehensive project based course where you will learn how to develop advanced AI applications using Large Language Models, integrate workflow using Langchain, and generate images using Generative Adversarial Networks. This course is a perfect combination between Python and artificial intelligence, making it an ideal opportunity to practice your programming skills while improving your technical knowledge in generative AI integration. In the introduction session, you will learn the basic fundamentals of large language models and generative adversarial networks, such as getting to know their use cases and understand how they work. Then, in the next section, you will find and download datasets from Kaggle, it is a platform that offers a diverse collection of datasets. Afterward, you will also explore Hugging Face, it is a place where you can access a wide range of ready to use pre-trained models for various AI applications. Once everything is ready, we will start building the AI projects. In the first section, we are going to build a legal document analyzer, where users can upload a PDF file, and AI will extract key information, summarize complex legal texts, and highlight important clauses for quick review. Next, we will develop an Excel data analyzer, enabling users to upload spreadsheets and leverage AI to identify trends, generate insights, and automate data analysis processes. Then after that, we will create an AI short story generator, where users can generate creative and engaging narratives based on simple prompts, making it a useful tool for writers and content creators. Following that, we will build an AI code generator, where users can input natural language Descriptions, and AI will generate structured, functional code snippets, streamlining the coding process. In the next section, we will develop a Q&A customer support chatbot, capable of answering common inquiries based on a given knowledge base, providing automated customer service responses. In addition, we will also create an AI-powered summarizer, designed to condense lengthy articles, research papers, or reports into concise summaries, helping users quickly understand key points. Moving on to LangChain, we will build a travel planner that takes user preferences and generates personalized itineraries, making trip planning easier and more efficient. Then, we will also create a math problem solver that interprets and solves mathematical equations step by step, helping students and professionals understand problem-solving techniques. In the following section, we will create GAN projects, for the first project, we will develop a random face generator, which can create realistic human faces from scratch, demonstrating the power of generative AI in producing lifelike imagery. In the second project, we will build a deep convolutional GAN from scratch by implementing the generator and discriminator functions, defining a loss function, and training the model using an adversarial learning approach to generate realistic images. Once we have built the apps we will conduct testing to make sure the app has been fully functioning and we will also deploy the app. Lastly, at the end of the course, we will build an LLM based app using no code tools like Dify AI and Relevance AI. By using these tools, you will be able to speed up the development process.First of all, before getting into the course, we need to ask ourselves this question, why should we build apps using a large language model? Well, here is my answer, LLMs can be used for analyzing context, automating complex text-based tasks, and generating human-like responses. These technologies not only streamline workflows and accelerate information retrieval but also improve accuracy in text generation and data processing.Whether it's content creation, document analysis, or chat-based interactions, LLMs make AI driven solutions more efficient and accessible.Below are things that you can expect to learn from this course:Learn the basic fundamentals of large language model and generative adversarial network, such as getting to know their use cases and understanding how they workLearn how to find AI models in Hugging Face and download dataset from KaggleLearn how to build legal document analyzer using LLMLearn how to analyze Excel data using LLMLearn how to build AI short story generator using LLMLearn how to build AI code generator using LLMLearn how to build customer support chatbot using LLMLearn how to build report summarizer using LLMLearn how to build AI travel planner using LangchainLearn how to build AI math solver using LangchainLearn how to build AI random face generator using ProGANLearn how to build random digital art generator using Deep Convolutional GANLearn how to build generator and discriminator functionsLearn how to train and fine tune GAN modelLearn how to create user interface using Streamlit and deploy app to Hugging Face SpaceLearn how to build LLM based apps using Dify AI and Relevance AI Who this course is for AI Engineers who are interested in building generative AI apps using LLMs and Langchain Data scientists who are interested in performing data augmentation using GANs Homepage: ?https://www.udemy.com/course/building-generative-ai-projects-with-llm-langchain-gan/ Rapidgator https://rg.to/file/4ca3f1324f27079c83d29e9b9cf28c8f/wtfje.Building.Generative.AI.Projects.with.LLM.Langchain.GAN.part1.rar.html https://rg.to/file/e6742a760f3520a851c745627f0733d0/wtfje.Building.Generative.AI.Projects.with.LLM.Langchain.GAN.part2.rar.html Fikper Free Download https://fikper.com/Z3M8XwCPxv/wtfje.Building.Generative.AI.Projects.with.LLM.Langchain.GAN.part1.rar.html https://fikper.com/tZIF4DaKWz/wtfje.Building.Generative.AI.Projects.with.LLM.Langchain.GAN.part2.rar.html No Password - Links are Interchangeable

Free Download Ai Automation - Build Llm Apps & Ai-Agents With N8N & Apis Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 12.00 GB | Duration: 13h 13m Automate Everything: n8n, LLMs, OpenAI API, Deepseek, Ollama & RAG! Business & Private AI Agents; More than just ChatGPT What you'll learn Fundamentals of Automation, AI Agents & LLMs (ChatGPT, Claude, Gemini, Deepseek, Llama, Mistral & more) Introduction to automation & key tools (n8n, Make, Zapier, LangChain, LangGraph, Flowise) Understanding and utilizing APIs for automation OpenAI API: Pricing structure, compliant usage & project setup Function calling with LLMs: Using calendars, emails, web search, webhooks, Airtable, Google Sheets & more Everything about vector databases, embedding models & Retrieval-Augmented Generation (RAG) n8n Basics & Automation Applications Basics of n8n: Installation, importing, exporting & selling workflows Automations with Airtable, Google Sheets & Google Cloud Using simple JavaScript variables in automation Expanding AI automation with LLMs: Email automation, sentiment analysis, databases Integrating open-source LLMs (Deepseek R1, Llama, Mistral) into automation Using external LLM APIs in n8n (Deepseek API, Groq API & more) AI Agents & RAG Chatbots in Workflows Integrating AI agents & RAG chatbots into workflows Automated vector database updates with Google Drive RAG chatbot with AI agent node, embeddings & retrieval techniques AI-powered email agents for automated summaries & responses Prompt engineering: Principles, best practices & avoiding errors Hosting, Social Media & Advanced Automations n8n self-hosting with Render & other options Using AI agents in WhatsApp, Telegram & social media Web scraping & automation with sub-workflows & webhooks Debugging strategies for error-free n8n automation Connecting Flowise AI agents with webhooks & Google Sheets Extending n8n with Flowise & JavaScript custom tools Business & Market Aspects of AI Automation AI automation as a business: Selling automation & AI agents Creating market-ready RAG bots for lead generation & website integration Marketing strategies for successfully selling AI solutions Optimizing RAG chatbots: Chunk size, overlap & data quality LlamaIndex & LlamaParse for data preprocessing in Google Colab Using Firecrawl for web data extraction in Markdown format Security, privacy & ethical concerns: Jailbreaks, prompt injections & data poisoning Copyright & data protection for AI-generated data Legal frameworks: EU AI Act & more Requirements No prior knowledge required, everything is shown step by step. Description AI Automation is the Future!But how does it really work? And how can AI optimize business processes-on a whole new level, far beyond ChatGPT? The answer: AI Agents.This course guides you through both essential and advanced concepts in automation using AI automation, AI agents, LLMs, vector databases, Retrieval-Augmented Generation (RAG), and n8n. You'll learn how to create powerful automations, build intelligent AI agents, and seamlessly integrate them into your workflows to enhance both business and personal projects.Additionally, you'll receive 29 downloadable JSON workflows to accelerate your learning and implementation.What You'll Learn in This Course:Fundamentals of Automation, AI Agents & LLMsDive into the world of AI automation:Introduction to automation, AI agents & essential tools (n8n, Make, Zapier, LangChain, LangGraph, Flowise).Understanding APIs and their role in automation.LLMs explained: ChatGPT, Claude, Gemini, Deepseek, Llama, Mistral & more.OpenAI API: Pricing structure, GDPR-compliant usage & project setup.Function calling with LLMs: How AI agents use tools like calendars, emails, web search, webhooks, Airtable, Google Sheets, and more.RAG (Retrieval-Augmented Generation): Vector databases & embeddings explained.n8n Basics: Installation & First WorkflowsMaster the fundamentals of n8n, the key to intelligent automation:Local installation with Node.js & using the web version without installation.Importing, exporting, and selling workflows.Setting up automations with Airtable, Google Sheets & Google Cloud.Using simple JavaScript variables in automation.Expanding AI Automation with LLMsBuild advanced AI-powered automations:Email automation with OpenAI API, Gmail, and Airtable.Real-time sentiment analysis & database storage.Integrating open-source LLMs (Deepseek R1, Llama, Mistral) into automation.Using any LLM API in n8n (Deepseek API, Groq API & more).Integrating AI Agents & RAG Chatbots into WorkflowsAutomate customer communication & data processing:RAG Agent: Automatically updating vector databases with Google Drive.RAG Chatbot using AI agent nodes, embeddings & retrieval techniques.AI-powered email agents for automated summaries & responses.Prompt Engineering for AI AgentsOptimize your prompts for better AI responses:Principles & best practices for effective prompt engineering.Avoiding errors & precisely controlling AI outputs.Hosting, Social Media & Advanced AutomationsExpand your automations with self-hosting & real-time integrations:n8n self-hosting with Render & other options.Using AI agents in WhatsApp & Telegram.Social media automation with sub-workflows, webhooks & web scraping.Debugging & Optimizing API IntegrationsEnhance performance & error handling in n8n workflows:Debugging strategies for error-free n8n automations.Connecting Flowise AI agents with webhooks & Google Sheets.Extending n8n with Flowise & JavaScript custom tools.Building a Business with AI Automation & AI AgentsLeverage your skills to create a profitable AI automation business:Selling automations & AI agents as services.Developing market-ready RAG bots for lead generation & website integration.Marketing strategies for successfully selling AI solutions.Optimizing RAG Chatbots: Data Quality & ChunkingImprove AI responses with optimized data strategies:Chunk size, overlap & data quality for better chatbot performance.Using Firecrawl for web data extraction in Markdown format.LlamaIndex & LlamaParse for data preprocessing in Google Colab.Security, Privacy & Ethical ConsiderationsProtect your AI agents & ensure GDPR compliance:Understanding & preventing jailbreaks, prompt injections & data poisoning.Ensuring copyright & data protection for AI-generated content.Key legal frameworks: EU AI Act & moreAdditionally, you'll gain access to 29 ready-to-use JSON workflows, available for download to streamline your learning experience and accelerate implementation.Become an Expert in AI Agents & Automation!After this course, you will have a deep understanding of AI automation, n8n, LLMs & RAG and be able to develop, optimize, and deploy powerful AI agents for business applications.Sign up now and step into the future of AI automation. Overview Section 1: Introduction Lecture 1 Welcome! Lecture 2 Course Overview Lecture 3 Important Tips for the Course Lecture 4 Explanation of the Links that you need in the Course Lecture 5 Important Links Section 2: Basics - Automation, LLMs, Function Calling, Vector Databases & RAG Explained Lecture 6 What to Expect in This Section Lecture 7 What Are Automations, AI Automation, and AI Agents? Lecture 8 What Is an API (Client and Server) Lecture 9 Tools for Automation & AI Agents: n8n, Make, Zapier, LangChain, Flowise & More Lecture 10 What are LLMs like ChatGPT, Claude, Gemini, Llama, Deepseek, Grok etc. Lecture 11 OpenAI API Explained: Pricing, Project Setup, Management & Compliance Lecture 12 Test Time Compute (TTS) Explained: Thinking Models like Deepseek R1 & OpenAI o3 Lecture 13 What is Function Calling in LLMs for AI Agents and AI Automations Lecture 14 Vector Databases, Embedding Models & Retrieval-Augmented Generation (RAG) Lecture 15 Key Takeaways Section 3: n8n Basics - Installation, Interface & First Simple Workflows Lecture 16 What This Section Covers Lecture 17 Local Installation of n8n with Node.js & Interface Overview Lecture 18 Managing Node Versions (Fixing Errors in n8n Installation) Lecture 19 Updating n8n Locally via Node.js Lecture 20 Testing n8n for Free Without Local Installation Lecture 21 First Automation: Automatically Save Bookings from On Form Submit in Airtable Lecture 22 Importing, Exporting, and Selling Workflows as JSON Lecture 23 Automatically Backing Up Airtable Data Locally Lecture 24 Connecting Google Sheets with n8n (Google Cloud Platform Console) Lecture 25 Recap Section 4: Expanding Automations with LLMs & AI Lecture 26 Overview of This Section Lecture 27 Email Automation for Customer Bookings with OpenAI (ChatGPT) Gmail & Airtable Lecture 28 Sentiment Analysis with LLMs & Storing Data in Airtable: OpenAI API Lecture 29 Using Open-Source LLMs with Ollama: Deepseek R1, Llama, Mistral & More Lecture 30 Integrating Any LLM into n8n via APIs: Deepseek API, Groq, Gemini, Claude & More Lecture 31 Recap of Automations with LLMs in n8n Section 5: AI Agents & RAG Chatbots in Your Automations & Email Automation Lecture 32 What to Expect in This Section Lecture 33 RAG Agent (Part 1): Automatic Vector Database Updates with Google Drive Lecture 34 RAG Chatbot (Part 2): AI Agent Node, Vector Database, Embeddings & More Lecture 35 Email Agent with Sub-Workflows, Vector Database, Google Sheets & More Lecture 36 The Fastest Way to Build an Email Agent! Lecture 37 Automatically Summarizing All New Emails of the Day with LLMs at 7AM Lecture 38 AI-Powered Email Automation: Filtering Messages & Auto-Replying Lecture 39 Recap & Practical Task Section 6: Prompt Engineering for AI Agents & AI Automations Lecture 40 Prompt Engineering for AI Agents & AI Automations (Systemprompts) Lecture 41 Key Principles of Prompt Engineering for AI Agents & Automations (Atricle) Section 7: Hosting & Tool Integration: Telegram, WhatsApp, Calendar, Scraping & More Lecture 42 Hosting n8n: Self-Hosting with Render & Other Options Lecture 43 Integrating AI Agents & Automations into WhatsApp Lecture 44 Code Snippet (Dynamic Expression) for WhatsApp as Download Lecture 45 Using AI Agents & Sub-Workflows with Telegram Trigger Node Lecture 46 Telegram Agent: Automating Emails, Calendars & More via Voice & Text Lecture 47 Push the Boundaries: Big AI-Agent that can talk and automate everything Lecture 48 More Practical Examples. Social Media Automation, Scraping, Crawling & More Lecture 49 My BEST Tip for Building and Prompting AI-Agents Lecture 50 Recap Section 8: Debugging Workflows & Integrating Other Apps/APIs with HTTP Requests & Webhooks Lecture 51 What to Expect: Debugging & Controlling n8n from Other Apps with Webhooks Lecture 52 Finding Errors in n8n Workflows with This Automation (Debugging n8n) Lecture 53 Flowise AI Agent & n8n Webhook: Integrate Sheets with JavaScript & HTTP request Lecture 54 JavaScript Code for the Flowise Custom Tool as Download (fetch for HTTP-Request) Lecture 55 One more example for Webhooks and HTTPS request Lecture 56 Recap of Webhooks, HTTP Requests and Error Trigger Nodes Section 9: Integrate Apps in Websites and Build a Business with AI Automation & AI Agents Lecture 57 Overview of This Section: AI Automation as a Business Lecture 58 What AI Automations & Agents Can Be Sold? Lecture 59 Market-Ready RAG Bot for Lead Generation (n8n, Pinecone & Google Sheets) Lecture 60 RAG Lead Bot as a Standalone App with a Published: URL Lecture 61 Integrating RAG Bots into Websites: HTML, WordPress & Custom CSS Branding Lecture 62 Selling Automations & AI Agents: Marketing, Offers, Price, Sales & More Lecture 63 Web Scraping with Software - Quickly Find Many Leads Lecture 64 Summary & Additional Tips Section 10: Optimizing RAG Chatbots - Data Quality, Chunk Size, Overlap, Embeddings & More Lecture 65 Optimizing RAG Chatbots: Data Quality, Chunk Size, Overlap, Embeddings & More Lecture 66 Scraping Webpages and Converting HTML & PDFs to Markdown for Better RAG Lecture 67 Efficient RAG with LlamaIndex & LlamaParse: Data Preparation for PDFs & CSVs Lecture 68 Chunk Size and Chunk Overlap for your Embeddings (Better RAG Application) Lecture 69 Recap: Data Quality, Chunk Size, Overlap, Embeddings for betther RAG Section 11: Problems, Security & Compliance - Copyright, Data Protection, GDPR & EU AI Act Lecture 70 First Problems and what will we Learn in this Section? Lecture 71 Jailbreaks: A Method to Hack LLMs and AI-Agents & Automations with Prompts Lecture 72 Prompt Injections: Another Security Vulnerability of LLMs, Agents & Automations Lecture 73 Data Poisoning and Backdoor Attacks Lecture 74 Copyrights & Intellectual Property of Generated Data from AI Agents Lecture 75 Privacy & Protection for your own and Client Data Lecture 76 Censorship, Alignment & Bias in LLMs: Deepseek, ChatGPT, Claude, Gemini, Dolphin Lecture 77 License of n8n: Can you sell AI agents, AI Automations or the Codebase from n8n? Lecture 78 EU & US Compliance: GDPR (DSGVO), CCPA/CPRA & the EU AI Act Lecture 79 Overview of the EU AI Act for ChatBots & AI Agents + CCPA/CPRA (Deep Research) Lecture 80 DSGVO (GDPR) Compliance for Chatbots & AI Agents + CCPA/CPRA (Deep Research) Lecture 81 Recap Important Points to Remember Section 12: What's Next? Lecture 82 Recap, Thank You & Next Steps For entrepreneurs who want to become more efficient, save money, or build an AI business.,For anyone eager to learn something new and gain deep insights into AI automation.,For individuals interested in AI and automation who want to build their own agents.,For developers and data scientists who want to stay on top of GenAI, automation, AI agents, and frameworks.,For anyone looking to automate tasks. 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Free Download Concept & Coding Llm Transformer,Attention, Deepseek Pytorch Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.28 GB | Duration: 3h 37m How does LLMs works, Understand Concept & Coding of Transformer,Attention, Deepseek using pytorch What you'll learn Learn how attention helps models focus on important text parts. Understand transformers, self-attention, and multi-head attention mechanisms. Explore how LLMs process, tokenize, and generate human-like text. Study DeepSeek's architecture and its optimizations for efficiency. Explore the transformer architecture Requirements python Description Welcome to this comprehensive course on how Large Language Models (LLMs) work! In recent years, LLMs have revolutionized the field of artificial intelligence, powering applications like ChatGPT, DeepSeek, and other advanced AI assistants. But how do these models understand and generate human-like text? In this course, we will break down the fundamental concepts behind LLMs, including attention mechanisms, transformers, and modern architectures like DeepSeek.We will start by exploring the core idea of attention mechanisms, which allow models to focus on the most relevant parts of the input text, improving contextual understanding. Then, we will dive into transformers, the backbone of LLMs, and analyze how they enable efficient parallel processing of text, leading to state-of-the-art performance in natural language processing (NLP). You will also learn about self-attention, positional encodings, and multi-head attention, key components that help models capture long-range dependencies in text.Beyond the basics, we will examine DeepSeek, a cutting-edge open-weight model designed to push the boundaries of AI efficiency and performance. You'll gain insights into how DeepSeek optimizes attention mechanisms and what makes it a strong competitor to other LLMs.By the end of this course, you will have a solid understanding of how LLMs work, how they are trained, and how they can be fine-tuned for specific tasks. Whether you're an AI enthusiast, a developer, or a researcher, this course will equip you with the knowledge to work with and build upon the latest advancements in deep learning and NLP. Let's get started! Overview Section 1: Introduction Lecture 1 Introduction to Course Section 2: Introduction to Transformer Lecture 2 AI History Lecture 3 Language as bag of Words Section 3: Transformer Embedding Lecture 4 Word embedding Lecture 5 Vector Embedding Lecture 6 Types of Embedding Section 4: Transformer -Encoder Decoder context Lecture 7 Encoding Decoding context Lecture 8 Attention Encoder Decoder context Section 5: Transformer Architecture Lecture 9 Transformer Architecture with Attention Lecture 10 GPT vs Bert Model Lecture 11 Context length and number of Parameter Section 6: Transformer -Tokenization code Lecture 12 Tokenization Lecture 13 Code Tokenization Section 7: Transformer model and block Lecture 14 Transformer architecture Lecture 15 Transformer block Section 8: Transformer coding Lecture 16 Decoder Transformer setup and code Lecture 17 Tranformer model download Lecture 18 Transformer model code architecture Lecture 19 Transforme model summary Lecture 20 Transformer code generate token Section 9: Attention-Intro Lecture 21 Transformer attention Lecture 22 Word embedding Lecture 23 Positional encoding Section 10: Attention-Maths Lecture 24 Attention Math Intro Lecture 25 Attention Query,Key,Value example Lecture 26 Attention Q,K,V transformer Lecture 27 Encoded value Lecture 28 Attention formulae Lecture 29 Calculate Q,K transpose Lecture 30 Attention softmax Lecture 31 Why multiply by V in attention Section 11: Attention-code Lecture 32 Attention code Overview Lecture 33 Attention code Lecture 34 Attention code Part2 Section 12: Mask Self Attention Lecture 35 Mask self attention Section 13: Mask Self Attention code Lecture 36 Mask Self Attention code Overview Lecture 37 Mask Self Attention code Section 14: Multimodal Attention Lecture 38 Encoder decoder transformer Lecture 39 Types of Transformer Lecture 40 Multimodal attention Section 15: Multi-Head Attention Lecture 41 Multi-Head Attention Lecture 42 Multi-Head Attention Code Part1 Section 16: Multi-Head Attention code Lecture 43 Multihead attention code Overview Lecture 44 Multi-head attention encoder decoder attention code Section 17: Deepseek R1 and R1-zero Lecture 45 Deepseek R1 training Lecture 46 Deepseek R1-zero Lecture 47 Deepseek R1 Architecture Lecture 48 Deepseek R1 Paper Section 18: Deepseek R1 Paper Lecture 49 Deepseek R1 paper Intro Lecture 50 Deepseek R1 Paper Aha moments Lecture 51 Deepseek R1 Paper Aha moments Part 2 Section 19: Bonus lecture Lecture 52 Deepseek R1 summary Generative AI enthusiasts Homepage: https://www.udemy.com/course/concept-coding-llm-transformerattention-deepseek-pytorch/ DOWNLOAD NOW: Concept & Coding Llm Transformer,Attention, Deepseek Pytorch Rapidgator https://rg.to/file/c88ac55d4c9026906ebe6ba7fd0a1253/cepxf.Concept..Coding.Llm.TransformerAttention.Deepseek.Pytorch.part2.rar.html https://rg.to/file/e558974e0224f11f8a68a7d565f52ad4/cepxf.Concept..Coding.Llm.TransformerAttention.Deepseek.Pytorch.part1.rar.html Fikper Free Download https://fikper.com/5oRB6pz55E/cepxf.Concept..Coding.Llm.TransformerAttention.Deepseek.Pytorch.part1.rar.html https://fikper.com/NozEJeR5MY/cepxf.Concept..Coding.Llm.TransformerAttention.Deepseek.Pytorch.part2.rar.html : No Password - Links are Interchangeable

Free Download Ai & Llm Engineering Mastery - Genai, Rag Complete Guide Published: 2/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 16.21 GB | Duration: 28h 11m From Fundamentals to Advanced AI Engineering - Fine-Tuning, RAG, AI Agents, Vector Databases & Real-World Projects What you'll learn Master the architecture and workflow of a RAG system for processing PDFs and multimodal data. Master the Fundamentals of AI, Machine Learning and Deep Learning (Basics) Master LangChain tools, frameworks, and workflows, including embedding techniques and retrievers. Fine-tuning models with OpenAI, LoRA, and other techniques to customize AI responses. Develop AI-driven applications with advanced RAG techniques, multimodal search, and AI agents for real-world use cases. Requirements Basics of Programming - Python Fundamentals INCLUDED Description Become an AI Engineer and master Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), AI agents, and vector databases in this comprehensive hands-on course. Whether a beginner or an experienced developer, this course will take you from zero to hero in building real-world AI-powered applications.This course combines deep theoretical insights with hands-on projects, ensuring you understand AI model architectures, development and optimization strategies, and practical applications.What You'll Learn:Deep Learning & Machine Learning FoundationsUnderstand neural networks, activation functions, transformers, and the evolution of AI.Learn how modern AI models are trained, optimized, and deployed in real-world applications.Master Large Language Models (LLMs) & Transformer-Based AIDeep dive into OpenAI models, and open-source AI frameworks.Build and deploy custom LLM-powered applications from scratch.Retrieval-Augmented Generation (RAG) & AI-Powered SearchLearn how AI retrieves knowledge using vector embeddings, FAISS, and ChromaDB.Implement scalable RAG systems for AI-powered document search and retrieval.LangChain & AI Agent WorkflowsBuild AI agents that autonomously retrieve, process, and generate information.Fine-Tuning LLMs & Open-Source AI ModelsFine-tune OpenAI, and LoRA models for custom applications.Learn how to optimize LLMs for better accuracy, efficiency, and scalability.Vector Databases & AI-Driven Knowledge RetrievalWork with FAISS, ChromaDB, and vector-based AI search workflows.Develop AI systems that retrieve and process structured & unstructured data.Hands-on with AI Deployment & Real-World ApplicationsBuild AI-powered chatbots, multimodal RAG applications, and AI automation tools.Who Should Take This Course?Aspiring AI Engineers & Data Scientists - Looking to master LLMs, AI retrieval, and search systems.Developers & Software Engineers - Who want to integrate AI into their applications.Machine Learning Enthusiasts - Seeking a deep dive into AI, GenAI, and AI-powered search.Tech Entrepreneurs & Product Managers - Wanting to build AI-driven SaaS products.Students & AI Beginners - Who need a structured, step-by-step path from beginner to expert.Course RequirementsNo prior AI experience required - the course takes you from beginner to expert.Basic Python knowledge (recommended but not required - Python Fundamentals Included in the course).Familiarity with APIs & JSON is helpful but not mandatory.A computer with internet access for hands-on development.Why Take This Course?Comprehensive AI Training: Covers LLMs, RAG, AI Agents, Vector Databases, Fine-Tuning.Hands-On Projects: Every concept is reinforced with real-world AI applications.Up-to-Date & Practical: Learn cutting-edge AI techniques & tools used in top tech companies.Zero to Hero Approach: Designed for absolute beginners & experienced developers alike.Master AI Engineering and become an expert in GenAI, LLMs, and RAG today. Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 DEMO - What You'll Build in this Course Lecture 3 Course Structure Lecture 4 How To Get The Most from This Course Section 2: Development Environment Setup Lecture 5 Development Environment Setup - Overview Lecture 6 Install Python on Windows - for WINDOWS USERS Lecture 7 Install Python on MAC - for MAC USERS Lecture 8 Download Visual Studio Code Lecture 9 Install the Python Extension Pack for VS Code Lecture 10 Running First Python Program in VS Code Section 3: Do You Know Python? Lecture 11 Python Deep Dive - Introduction and Overview Section 4: OPTIONAL - Python Deep Dive - Master Python Fundamentals Lecture 12 What is Python and Where It's Used? Lecture 13 Python Compilation & Interpretation Process Lecture 14 Download Python Fundamentals Code Lecture 15 Declaring Variables in Python Lecture 16 Data Types Lecture 17 Python f-Strings Lecture 18 Numbers - Integers and Floats Lecture 19 Introduction to Lists - Accessing and Modifying Them Lecture 20 f-Strings & Individual Values from a List Lecture 21 Sorting a List and Getting a List Length Lecture 22 Lists and Loops - Looping through a List Lecture 23 Making a List of Numbers with Loops and the Range Function Lecture 24 Statistics Functions for Numbers Lecture 25 Generate Even Numbers with the List and Range Lecture 26 Important: Code Organization Note Lecture 27 List Comprehension Lecture 28 Tuples Lecture 29 Branching - If Statements and Booleans Lecture 30 The Elif and the in Keywords Lecture 31 Hands-on - Using AND and OR Logical Operators Lecture 32 AND OR Logical Operators Lecture 33 Checking for Inequalities Lecture 34 Hands-on - Inner If-Statements Lecture 35 Data Structures - Dictionaries - Introduction and Declaring and Accessing Values Lecture 36 Modifying a Dictionary Lecture 37 Iterating Through a Dictionary Lecture 38 Nested Dictionaries and Looping Through Them Lecture 39 Looping through a Dictionary with a List Inside Lecture 40 User Input and While Loops - User Input - Introduction Lecture 41 Hands-on - Odd or Even Number Lecture 42 While Loops & Simple Quit Program Lecture 43 Hands-on - Quiz Game Lecture 44 Removing all Instances of Specific Values from a List Lecture 45 Hands-on Dream Travel Itinerary Program - Filling a Dictionary with User Input Lecture 46 Functions - Introduction Lecture 47 Passing Information to a Function (parameters) Lecture 48 Positional and Named Arguments Lecture 49 Default Values - Parameters Lecture 50 Return Values from a Function Lecture 51 Hands-on - Returning an Integer & Intro do DocString Lecture 52 Functions - Passing a List as Argument Lecture 53 Passing an Arbitrary Number of Arguments to a Function Lecture 54 Introduction to Modules - Importing Specific functions from a Module Lecture 55 Using the "as" as an Alias Lecture 56 Classes and OOP - Object Oriented Programming - The "init and "str" methods Lecture 57 Adding More Methods to the Class Lecture 58 Setting a Default Value for an Attribute Lecture 59 Modifying Class Attribute - directly and with Methods Lecture 60 Inheritance - Create an Ebook - Child Class Lecture 61 Overriding Methods Lecture 62 Creating and Importing from a Module Lecture 63 The Object Class - Overview Lecture 64 The Python Standard Library Lecture 65 Random Module - Random Fruit Hands-on Lecture 66 Hands-on - Random Fruit with Choice Module Method Lecture 67 Using Datetime Module Lecture 68 Writing & Reading Files - Do Useful Tasks with Python - Do amazing things Lecture 69 The Path Class & Reading a Text File Lecture 70 Resolving Path - Reading From a Subdirectory with Path Lecture 71 Path Properties Overview Lecture 72 Writing to Text file with Path Lecture 73 Read and Write to File Using the "with" Keyword Lecture 74 Handling Exceptions Lecture 75 The "FileNotFound" and "IndexError" Exceptions Types Lecture 76 Custom Exception Creation and handling Lecture 77 JSON - Reading and Writing to a JSON File Lecture 78 Hands-on - Writing and Reading - Countries to JSON file Lecture 79 Hands-on - File Organizer Lecture 80 Python Virtual Environment and PIP Lecture 81 Setting up Virtual Environment and Installing a Package Lecture 82 Hands-on Watermarker Python Tool Lecture 83 Building an Image Watermarker in Python - Part 1 Lecture 84 Generating the Watermarked Images Lecture 85 Reading CSV File - Introduction Lecture 86 Getting the CSV header Position Lecture 87 Reading Data from a CSV Column Lecture 88 Plotting a Graph with CSV Data Section 5: Deep and Machine Learning Deep Dive Lecture 89 Deep and Machine Learning Deep Dive - Overview and Breakdown Lecture 90 Deep Learning Key Aspects Lecture 91 Deep Neural Network Dissection - Full Dive with Analogies Lecture 92 The Single Neuron Computation - Deep Dive Lecture 93 Wights - Deep Dive Lecture 94 Activation Functions - Deep Dive with Analogies Lecture 95 Deep Learning Summary Lecture 96 Machine Learning Introduction - Machine Learning vs. Deep Learning Lecture 97 Learning Types - Education System Analogy Lecture 98 Comparative Capabilities Deep Learning and Machine Learning and AI - Summary Section 6: Generative AI (GenAI) - Deep Dive Lecture 99 GenAI Introduction and Architecture Overview Lecture 100 GenAI Key Technologies - Limitations and challenges Lecture 101 GenAI Key Components Overview and Summary Section 7: LLMs (Large Language Models) - Fundamentals - A Deep Dive Lecture 102 LLMs - Overview Lecture 103 The Transformer Architecture - Fundamentals Lecture 104 The Self-Attention Mechanism - Analogy Lecture 105 The Transformers Library - Deep Dive Lecture 106 HANDS-ON - Create a Simple LLM from the Transformers Library - Simple Lecture 107 HANDS-ON - Hands-on Enhanced Transformers LLM Lecture 108 Open-source vs. Closed-source Models - Overview Section 8: OpenAI Models and Setup Lecture 109 Setup OpenAI Account and API Key Lecture 110 Using APIs Effectively in AI Projects Lecture 111 HANDS-ON - Making our First Call to OpenAI Model Section 9: Prompt Engineering - Communicating with LLMs - Deep Dive Lecture 112 Prompt Engineering Introduction Lecture 113 Prompt Engineering and Types - Why it Matters Lecture 114 HANDS-ON - Simple Prompting Example Lecture 115 Advanced Prompting Techniques and Challenges Lecture 116 HANDS-ON - Few-shots Prompting Lecture 117 HANDS-ON - Zero-shot Prompting Lecture 118 HANDS-ON -Chain-of-Thoughts Prompting Lecture 119 HANDS-ON - Instructional Prompting Lecture 120 HANDS-ON - Role-Playing and Open-ended Prompting Lecture 121 Temperature and Top-p Sampling Lecture 122 HANDS-ON - Prompt Techniques Combination and Streaming Lecture 123 Prompt Engineering Summary and Takeaways Section 10: Ollama & Open-Source Models - Complete Guide Lecture 124 Ollama - Introduction Lecture 125 Download Source Code and Resources Lecture 126 Ollama Deep Dive - Ollama Overview - What is Ollama and Advantages Lecture 127 Ollama Key Features and Use Cases Lecture 128 System Requirements & Ollama Setup - Overview Lecture 129 HANDS-ON - Download and Setup Ollama and Llama3.2 Model Lecture 130 Ollama Models Page - Overview Lecture 131 Ollama Model Parameters Deep Dive Lecture 132 Understanding Parameters and Disk Size and Computational Resources Needed Lecture 133 Ollama CLI Commands -Pull and Testing a Model Lecture 134 Pull in the Llava Multimodal Model and Caption an Image Lecture 135 Summarization and Sentiment Analysis & Customizing Our Model Lecture 136 Ollama REST API - Generate and Chat Endpoints Lecture 137 Ollama REST API - Request JSON Mode Lecture 138 Ollama Models Support Different Tasks - Summary Lecture 139 Different Ways to Interact with Ollama Models Lecture 140 Ollama Model Running Under Msty App Lecture 141 Ollama Python SDK for Building LLM Local Applications Lecture 142 HANDS-ON - Interact with Llama3 in Python Using Ollama REST API Lecture 143 Ollama Python Library - Chatting with a Model Lecture 144 Chat Example with Streaming Lecture 145 Using Ollama Show Function Lecture 146 Create a Custom Model in Code Section 11: Context & Memory Management for LLMs - Deep Dive Lecture 147 HANDS-ON - Context and Memory Management Overview Lecture 148 What is Context and Memory Management - Deep Dive Lecture 149 HANDS-ON - Adding Memory and Context to Chatbox Lecture 150 Summary Section 12: Logging in LLM Applications - Deep Dive Lecture 151 Logging - Introduction - What and the Why Lecture 152 Logging in LLM Applications and Logging Life Cycle Lecture 153 HANDS-ON - Chatbot with Logging Lecture 154 Summary Section 13: RAG - Retrieval-Augmented Generation - Deep Dive Lecture 155 RAG Introduction - What is it? Lecture 156 RAG Key Components - The RAG Triad Lecture 157 RAG vs. Pure GenAI Models Lecture 158 RAG Deep Dive - Full Diagram Walkthrough Lecture 159 RAG Benefits and Practical Applications Lecture 160 RAG Challenges Lecture 161 RAG Fundamentals - Takeaways - Summary Section 14: Vector Databases and Embeddings - Deep Dive Lecture 162 Vector Databases and Embeddings for RAG Workflows - Introduction Lecture 163 Download Source code Lecture 164 Introduction to Vector Databases - Full Overview Lecture 165 Why Vector Databases Lecture 166 Vector Databases - Benefits and Advantages Lecture 167 Traditional vs. Vector Databases - Limitations and challenges Lecture 168 Vector Databases & Embeddings - Full Overview Lecture 169 Embeddings vs. Vectors - Differences Lecture 170 Vector Databases - How They Work and Advantages Lecture 171 Vector Databases Use Cases Lecture 172 Vector and Traditional Databases - Summary Lecture 173 The Top 5 Vector Databases - Overview Lecture 174 Building Vector Databases - Dev Environment Setup Lecture 175 Setup VS-Code, Python and OpenAI API Key Lecture 176 Chroma Database workflow Lecture 177 Creating a ChromaDB and Adding Documents and Querying Lecture 178 Looping Through the Results & Showing Similarity Search Results Lecture 179 Chroma Default Embedding Function Lecture 180 Chroma Vector Database - Persisting Data and Saving Lecture 181 Creating an OpenAI Embeddings - Raw without Chroma Lecture 182 Using OpenAIs Embedding API to Create Embedding in ChromaDB Lecture 183 Vector Databases Metrics and Data Structures Lecture 184 Summary Lecture 185 Vector Similarity Deep Dive - Cosine Similarity Lecture 186 Eucledian Distance - L2 Norm Lecture 187 Dot Product Lecture 188 Summary Lecture 189 Vector Databases and LLM - Deep Dive Lecture 190 Loading all Documents Lecture 191 Generating Embeddings from Documents and Insert to Vector Database Lecture 192 Getting the Relevant Chunks when Given a Query Lecture 193 Using OpenAI LLM to Generate Response - Full Workflow Lecture 194 Summary Section 15: HANDS-ON - RAG PDF Workflow - Build RAG Workflows Deep Dive Lecture 195 Building a RAG Pipeline - Overview Lecture 196 First RAG Workflow Architectural Diagram Lecture 197 Setting up the Embedding Model Class Lecture 198 HANDS-ON - Building and Showcasing the RAG Workflow Lecture 199 HANDS-ON - RAG Workflow with UI - Streamlit Lecture 200 First RAG Pipeline Summary Section 16: HANDS-ON - Build a PDF RAG System with Text Chunking Lecture 201 PDF RAG Workflow - Architecture Overview Lecture 202 PDF and Chunk Processing and Chunk Overlap - Deep Dive Lecture 203 Setting up the SimpleRAGSystem Class and Methods Lecture 204 Testing the PDF RAG System Lecture 205 Simple PDF RAG Workflow - Summary Section 17: LLM Tools and Frameworks - LangChain Deep Dive Lecture 206 LLM Frameworks Introduction - LangChain Fundamentals Lecture 207 What is LangChain and and Main Components Lecture 208 LangChain Setup and ChatModel Lecture 209 Hands-on - LangChain ChatPromptTemplates Lecture 210 Indexes, Retrievers and Data Preparation - Overview Lecture 211 Hands-On - LangChain TextLoaders Lecture 212 Hands-on: Text Splitting and Cleaning Lecture 213 Hands-on: Embeddings and Retriever with FAISS VectorStore Lecture 214 LangChain TextSplitter - Deep Dive Lecture 215 LangChain DirectoryLoader Lecture 216 LangChain PDFLoader Lecture 217 Hands-on: LangChain Chains Lecture 218 Hands-on - Simple RAG System with Chat and LangChain Chains Lecture 219 Hands-on: Full RAG System QA Bot Using LangChain Section 18: HANDS-ON - Building LLM Applications with LangChain Lecture 220 LLM Application - News Summarizer - Architectural Overview Lecture 221 News Summarizer - Full Implementation Lecture 222 LLM Application - Youtube Video Summarizer - Architectural Overview Lecture 223 Youtube Video Summarizer & Q&A Dependency Setup Lecture 224 Youtube Video Summarizer Class Setup and Walkthrough Lecture 225 Youtube Video Summarizer Q&A - Testing the Workflow Lecture 226 LLM Application - Voice Assistant RAG System - Architectural Overview Lecture 227 Voice Assistant RAG System - Demo Lecture 228 Voice Assistant RAG System - Walkthrough and Demo Section 19: Advanced RAG Techniques - Naive vs Advanced RAG Techniques Lecture 229 RAG and the RAG Triad - Quick Overview and Recap Lecture 230 What is RAG and Naive RAG Overview and Pitfalls - Motivation Lecture 231 Deep Dive into Each Naive RAG Drawbacks Lecture 232 Advanced RAG Technique - Query Expansion with Multiple Queries - Overview Lecture 233 Hands-on - Query Expansion with Multiple Queries - Generate Multiple Queries Lecture 234 Query Expansion Workflow Architectural Diagram Lecture 235 Hands-on- Setting up the Workflow and Code Walkthrough Lecture 236 Query Expansion Full RAG Workflow Lecture 237 Query Expansion with Multiple Queries Downsides & Summary Lecture 238 Re-Ranking & Cross-encoder and Bi-encoders - Overview Lecture 239 Reranking Technique RAG System Workflow Architecture Lecture 240 Cohere Rerank API Key Setup Lecture 241 Hands-on - Re-ranking Implementation with Cohere - Full Implementation Lecture 242 Re-ranking Summary Section 20: Multimodal RAG - Deep Dive Lecture 243 Multimodal RAG Source Code Lecture 244 RAG & Multimodal RAG - Recap and Overview Lecture 245 RAG Benefits and Practical Applications Lecture 246 Multimodal RAG - Overview & Motivation and Benefits - How it Works Lecture 247 How Search Is Integrated into a Multimodal RAG System - Full Workflow Lecture 248 Why Multimodal Search is so Powerful Lecture 249 Visual Explanation Why Multimodal Search is so Powerful Lecture 250 HANDS-on: Multimodal Search System setup - Create Embeddings from Images Lecture 251 Finish the Multimodal Search System Lecture 252 HANDS-ON - Multimodal Recommender System - Overview Lecture 253 Getting our Dataset from HuggingFace & showing Number of Rows Lecture 254 Saving Images Embeddings to Vector Database Lecture 255 Testing our MultiModal Recommender System - Fetching the Correct Images Lecture 256 Setting up the RAG Workflow Lecture 257 Putting it all Together and Testing the Multimodal Recommender RAG System Lecture 258 Adding a Streamlit UI to the Multimodal Recommender System Section 21: AI Agents & Agentic Workflows - Deep Dive Lecture 259 AI Agents Deep Dive - A Full Overview Lecture 260 Agents Characteristics and Use Cases Lecture 261 Download Source Code for AI Agents Section Lecture 262 Building our First AI Agent - Project Setup (OpenAI API) Lecture 263 Build our First AI Agent - Creating the Agent Class and Prompt Lecture 264 First AI Agent - Running our First Agent and Seeing the Results Lecture 265 Passing Complex Queries Through the Agent Lecture 266 First Agent - Using a Loop to Automate our Agent Lecture 267 Adding Interactive to Our Agent - Console App Lecture 268 Agent Introduction - Section Summary Lecture 269 LangGraph - Overview & Key Concepts Lecture 270 LangGraph - How It Helps Build AI Agents Lecture 271 LangGraph Core Concepts - Simple Flow Diagrapm Lecture 272 LangGraph - Data and State - Overview Lecture 273 Building a Simple Agent with LangChain Lecture 274 LangGraph Simple Bot - Streaming Values - Console App Lecture 275 Adding Tools to our Basic LangGraph Agent Lecture 276 Adding tools to the Agent - Part 1 Lecture 277 Adding Tools to the Agent - Using Built-in Tools - Part 2 Lecture 278 Adding Memory to Our Agent State Lecture 279 Adding Human-in-the-loop to the AI Agent Lecture 280 Building AI Agents with LangChain - Section Summary Lecture 281 Hands-on - Build a Financial Report Writer AI Agent Lecture 282 Agent State and Prompts Setup Lecture 283 Creating All Nodes - Functions Lecture 284 Adding Nodes and Edges and Running our Agent Lecture 285 Adding a GUI to the Agent with Streamlit Lecture 286 Optimization Techniques - Overview Lecture 287 Financial Report Writer AI Agent - Course Summary Section 22: Fine-tuning LLMs Lecture 288 Fine-tuning Introduction - Overview Lecture 289 Fine-tuning Techniques - Overview Lecture 290 Fine-tuning Comparison of Techniques Lecture 291 Fine-tuning General Process - Overview Lecture 292 Fine-tuning OpenAI Models Pricing Lecture 293 Tokens and the Tokenizer OpenAI Tool Lecture 294 HANDS-ON - Fine-tuning an OpenAI Model - Full Walkthrough Lecture 295 Crating a Chatbot with our Fine-tuned Model and Testing Section 23: Fine-Tuning Technique - LoRA Deep Dive Lecture 296 LoRA Introduction - Benefits Lecture 297 LoRA Deep Analysis Lecture 298 LoRA Implementation Strategy Workflow Lecture 299 Hands-on - Training Models - LoRA and PEFT Lecture 300 Running LoRA Model Fine-tuning and Testing Lecture 301 Creating an API Service to Interface with Our Fine-tuned Models Lecture 302 Testing our LoRA Model API Endpoint Lecture 303 Chatting with LoRA Fine-tuned Models Lecture 304 Full LoRA Workflow - Train and Chat with Fine-tuned Models Section 24: Wrap up and Next Steps Lecture 305 Wrap up and Next Steps Developers looking to implement AI-powered document search and retrieval.,Tech Entrepreneurs & Product Managers who want to build AI-driven applications.,Students & Researchers exploring the practical applications of LLMs and AI-driven automation. 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Free Download Master Llm Optimization Boost Ai Performance & Efficiency Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.89 GB | Duration: 3h 4m Unlock advanced techniques for fine-tuning, scaling, and optimizing LLMs to enhance AI capabilities What you'll learn Learn to use Google Colab for unleashing the power of Python's text analysis and deep learning ecosystem Introduction to the basic concepts around LLMs and Generative AI Get acquainted with common Large Language Model (LLM) frameworks including LangChain Learning about using the Hugging Face hub for accessing different LLMs Introduction to the theory and implementation of LLM Optimization Requirements Prior experience of using Jupyter notebooks Prior exposure to Natural Language Processing (NLP) concepts will be helpful but not compulsory An interest in using Large Language Models (LLMs) for your own documents Description Master LLM Optimization: Boost AI Performance & EfficiencyUnlock the power of Large Language Models (LLMs) with our cutting-edge course, "Master LLM Optimization: Boost AI Performance & Efficiency." Designed for AI enthusiasts, data scientists, and developers, this course offers an in-depth journey into LLMs, focusing on optimization techniques that elevate AI capabilities. Whether you're a beginner in LLM implementation or an experienced practitioner seeking to refine your skills, this course equips you with the knowledge and tools to excel in this rapidly evolving field.Course Overview:This course deep dives into LLM frameworks like OpenAI, LangChain, and LLAMA-Index, empowering you to build and fine-tune AI solutions like Document-Reading Virtual Assistants. With a comprehensive curriculum, you'll explore the theory and practical implementation of LLM optimization, gaining hands-on experience with popular LLM models like GPT and Mistral through Hugging Face. By the end of the course, you'll have mastered advanced techniques for harnessing LLMs, enabling you to develop AI systems that are both efficient and powerful.Key Learning Outcomes:Foundations of Generative AI and LLMs: Understand the core concepts of Gen AI and LLMs, laying a solid foundation for more advanced topics.Introduction to LLM Frameworks: Get hands-on experience with popular LLM frameworks, including OpenAI, LangChain, and LLAMA-Index, enabling you to build and deploy AI applications with ease.Accessing LLM Models: Learn how to access LLM models via Hugging Face, work with cutting-edge models like Mistral, and implement them effectively.LLM Optimization Techniques: Discover advanced optimization methods such as quantization, fine-tuning, and scaling, essential for enhancing LLM performance in real-world applications.Retrieval-Augmented Generation (RAG): Gain insights into RAG and its role in LLM optimization, enabling more accurate and efficient AI responses.Leveraging LLM Tools for Summarization & Querying: Master using LLM tools for abstract summarization and querying, ensuring you can harness the full potential of large language models.Why Enroll?Guided by an expert instructor with an MPhil from the University of Oxford and a data-intensive PhD from Cambridge University, this course offers unparalleled expertise in LLM optimization. You'll benefit from a supportive learning environment, practical assignments, and a community of AI enthusiasts, ensuring a comprehensive understanding of LLM implementation.Ready to Become an LLM Expert?Enrol now to transform your AI capabilities, master LLM optimization techniques, and unlock the potential of text data with large language models. Join us and elevate your expertise in AI today! Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 Data and Code Lecture 3 What is Google Colab? Lecture 4 Google Colabs and GPU Lecture 5 Installing Packages In Google Colab Lecture 6 Read in a PDF Lecture 7 Read in Multiple PDFs Section 2: Welcome to the World of Gen-AI and LLMs Lecture 8 Lowdown on GenAI Models Lecture 9 More on Gen-AI Lecture 10 How Does Gen AI Work Lecture 11 What are GPTs? Lecture 12 Interplays Between Gen-AI and LLMs Lecture 13 Introduction to Open API Lecture 14 Other LLMs Lecture 15 Start With Hugging Face Lecture 16 Access and Use Other LLMs Via Hugging Face Lecture 17 Access Mistral LLM With Hugging Face Lecture 18 LLMs on Google Cloud Computing (GCP) Section 3: Start With Large Language Models (LLMs) Lecture 19 LLM Workflow Lecture 20 Overview of Summarization Lecture 21 Abstract Summarization Lecture 22 Langchain Tech Lecture 23 Langchain QA Lecture 24 Introduction to Llama Lecture 25 Llama- Another LLM Implementation Section 4: Introduction to Prompt Engineering Lecture 26 Get Prompting Lecture 27 More Prompting Section 5: LLM Optimisation- An Overview Lecture 28 LLM Optimisation-Theory Lecture 29 Basic Quantisation- A Quick Implementation Lecture 30 Stochastic Gradient Descent (SGD) For LLMs-Theory Lecture 31 SGD Implementation For LLM Optimisation Lecture 32 RAGs and Their Roles in LLM Optimisation- Theory Lecture 33 A RAG Workflow Lecture 34 Prepare The External Text Data For Use in RAG Lecture 35 Create and Retrieve Embeddings Lecture 36 Retrieval Lecture 37 More Detailed Queries Section 6: Miscallaneous Lecture 38 Gen AI Lecture 39 Go Home- You Are Drunk Lecture 40 Another Jupyter Option Lecture 41 Memory Management Students with prior exposure to NLP analysis,Those interested in using LLM frameworks for learning more about your texts,Students and practitioners of Artificial Intelligence (AI) Screenshot Homepage https://www.udemy.com/course/master-llm-optimization-boost-ai-performance-efficiency/ Rapidgator https://rg.to/file/266ec4a3f243920e42556fe41b6bdcf0/kytci.Master.Llm.Optimization.Boost.Ai.Performance..Efficiency.part1.rar.html https://rg.to/file/f336cc90101de994eeab17f62aa3a196/kytci.Master.Llm.Optimization.Boost.Ai.Performance..Efficiency.part2.rar.html Fikper Free Download https://fikper.com/1IT4jcgtU6/kytci.Master.Llm.Optimization.Boost.Ai.Performance..Efficiency.part1.rar.html https://fikper.com/h1Mke3bAZv/kytci.Master.Llm.Optimization.Boost.Ai.Performance..Efficiency.part2.rar.html No Password - Links are Interchangeable

Free Download Gen AI - LLM RAG Two in One - LangChain + LlamaIndex Published 10/2024 Created by Manas Dasgupta MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 27 Lectures ( 9h 13m ) | Size: 4.44 GB Gen AI - Learn to develop RAG Applications using LangChain an LlamaIndex Frameworks using LLMs and Vector Databases What you'll learn Be able to develop your own RAG Applications using either LangChain or LlamaIndex Be able to use Vector Databases effectively within your RAG Applications Craft Effective Prompts for your RAG Application Create Agents and Tools as parts of your RAG Applications Create RAG Conversational Bots Perform Tracing for your RAG Applications using LangGraph Requirements Python Programming Knowledge Description This course leverages the power of both LangChain and LlamaIndex frameworks, along with OpenAI GPT and Google Gemini APIs, and Vector Databases like ChromaDB and Pinecone. It is designed to provide you with a comprehensive understanding of building advanced LLM RAG applications through in-depth conceptual learning and hands-on sessions. The course covers essential aspects of LLM RAG apps, exploring components from both frameworks such as Agents, Tools, Chains, Memory, QueryPipelines, Retrievers, and Query Engines in a clear and concise manner. You'll also delve into Language Embeddings and Vector Databases, enabling you to develop efficient semantic search and similarity-based RAG applications. Additionally, the course covers various Prompt Engineering techniques to enhance the efficiency of your RAG applications.List of Projects/Hands-on included: Develop a Conversational Memory Chatbot using downloaded web data and Vector DBCreate a CV Upload and Semantic CV Search App Invoice Extraction RAG AppCreate a Structured Data Analytics App that uses Natural Language Queries ReAct Agent: Create a Calculator App using a ReAct Agent and ToolsDocument Agent with Dynamic Tools: Create multiple QueryEngineTools dynamically and orchestrate queries through AgentsSequential Query Pipeline: Create Simple Sequential Query PipelinesDAG Pipeline: Develop complex DAG PipelinesDataframe Pipeline: Develop complex Dataframe Analysis Pipelines with Pandas Output Parser and Response SynthesizerWorking with SQL Databases: Develop SQL Database ingestion BotThis twin-framework approach will provide you with a broader perspective on RAG development, allowing you to leverage the strengths of both LangChain and LlamaIndex in your projects. Who this course is for Software Developers, Data Scientists, ML Engineers, DevOps Engineers, Support Engineers, Test / QA Engineers Homepage https://www.udemy.com/course/llm-rag-langchain-llamaindex/ Screenshot Rapidgator https://rg.to/file/7230881180fe2e920d73cb67f10d58f5/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part2.rar.html https://rg.to/file/7b692f6bb84c20b1ae035f2a1a779a01/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part4.rar.html https://rg.to/file/8462791b936870fe70b703ba287bf8e7/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part1.rar.html https://rg.to/file/e43c085f370c303e6c476ae920466a80/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part5.rar.html https://rg.to/file/fc2f130578d5c0ff7724b6095f427297/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part3.rar.html Fikper Free Download https://fikper.com/0MdfBVuUR1/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part1.rar.html https://fikper.com/9vG7HFRHEl/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part2.rar.html https://fikper.com/DlsgBhYalk/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part3.rar.html https://fikper.com/QCriLU0toN/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part4.rar.html https://fikper.com/w5pOwFLOz3/bcoul.Gen.AI..LLM.RAG.Two.in.One..LangChain..LlamaIndex.part5.rar.html No Password - Links are Interchangeable

Free Download Build local LLM applications using Python and Ollama Published 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 1h 51m | Size: 728 MB Learn to create LLM applications in your system using Ollama and LangChain in Python | Completely private and secure What you'll learn Download and install Ollama for running LLM models on your local machine Set up and configure the Llama LLM model for local use Customize LLM models using command-line options to meet specific application needs Save and deploy modified versions of LLM models in your local environment Develop Python-based applications that interact with Ollama models securely Call and integrate models via Ollama's REST API for seamless interaction with external systems Explore OpenAI compatibility within Ollama to extend the functionality of your models Build a Retrieval-Augmented Generation (RAG) system to process and query large documents efficiently Create fully functional LLM applications using LangChain, Ollama, and tools like agents and retrieval systems to answer user queries Requirements Basic Python knowledge is recommended, but no prior AI experience is required. Description If you are a developer, data scientist, or AI enthusiast who wants to build and run large language models (LLMs) locally on your system, this course is for you. Do you want to harness the power of LLMs without sending your data to the cloud? Are you looking for secure, private solutions that leverage powerful tools like Python, Ollama, and LangChain? This course will show you how to build secure and fully functional LLM applications right on your own machine.In this course, you will:Set up Ollama and download the Llama LLM model for local use.Customize models and save modified versions using command-line tools.Develop Python-based LLM applications with Ollama for total control over your models.Use Ollama's Rest API to integrate models into your applications.Leverage LangChain to build Retrieval-Augmented Generation (RAG) systems for efficient document processing.Create end-to-end LLM applications that answer user questions with precision using the power of LangChain and Ollama.Why build local LLM applications? For one, local applications ensure complete data privacy-your data never leaves your system. Additionally, the flexibility and customization of running models locally means you are in total control, without the need for cloud dependencies.Throughout the course, you'll build, customize, and deploy models using Python, and implement key features like prompt engineering, retrieval techniques, and model integration-all within the comfort of your local setup.What sets this course apart is its focus on privacy, control, and hands-on experience using cutting-edge tools like Ollama and LangChain. By the end, you'll have a fully functioning LLM application and the skills to build secure AI systems on your own.Ready to build your own private LLM applications? Enroll now and get started! Who this course is for Software developers who want to build and run private LLM applications on their local machines. Data scientists looking to integrate advanced LLM models into their workflow without relying on cloud solutions. Privacy-focused professionals who need to maintain complete control over their data while leveraging powerful AI models. Tech enthusiasts interested in exploring local LLM setups using cutting-edge tools like Ollama and LangChain. Homepage https://www.udemy.com/course/build-local-llm-applications-using-python-and-ollama/ Rapidgator https://rg.to/file/3ee1f5ccc3bffa5be851bfae3824fa1b/pjgxc.Build.local.LLM.applications.using.Python.and.Ollama.rar.html Fikper Free Download https://fikper.com/DfMp8VCwiy/pjgxc.Build.local.LLM.applications.using.Python.and.Ollama.rar.html No Password - Links are Interchangeable

Free Download ZerotoMastery - Developing LLM App Frontends with Streamlit Released 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 20 Lessons ( 1h 44m ) | Size: 280 MB This byte-sized course will teach Streamlit fundamentals and how to use Streamlit to create a frontend for your LLM-powered applications. In this project-based course you'll learn to use Streamlit to create a frontend for an LLM-powered Q&A application. Streamlit is an open-source Python library that simplifies the creation and sharing of custom frontends for machine learning and data science apps with the world. What you'll learn How to utilize Streamlit to develop intuitive frontends for machine learning and data science applications, making your projects accessible to a wider audience The basics of Streamlit, including its installation and core features, tailored for beginners to quickly start building interactive web apps Integrating Large Language Models (LLMs) with Streamlit to create consumer-facing Q&A applications, leveraging the power of AI to answer user queries in real-time Transitioning from Jupyter Notebooks to a production-ready web app using Streamlit, enabling you to share your LLM-powered applications with the world beyond the developer community Why Learn Streamlit? Large Language Models (LLMs) are the latest technological revolution, and you've probably heard a lot about harnessing the power of LLMs to use them in AI application. But in order to make your AI application easy to use for users, you'll want a frontend that easily integrates with your LLM and provides a seamless experience for your users. That's where Streamlit comes in. Streamlit is an amazing open-source Python library that provides a fast way to build and share machine learning and data science applications with the world. This Project starts with a section that teaches you everything you need to know about Streamlit, specifically designed for beginners. Then in the second section we'll jump into building the frontend for your LLM-powered Q&A App. Wait... What's a Project? One of the most common things we hear from students is: "I want to build more projects!". We love hearing that, because building projects is really the best way to learn. And unique, challenging projects can really make your portfolio stand out for potential employers. But also...it just feel so good when you actually build something real! That's why we've created ZTM Projects. A collection of comprehensive portfolio and practice projects that you can use to advance your knowledge, learn new skills, build your portfolio, and sometimes even just have fun! Homepage https://zerotomastery.io/courses/learn-streamlit-tutorial/ TakeFile https://takefile.link/fpwo3n8h0hte/sbbhd.ZerotoMastery..Developing.LLM.App.Frontends.with.Streamlit.rar.html Rapidgator http://peeplink.in/83a5f4bd243b Fikper Free Download https://fikper.com/X4XFcPEa0p/sbbhd.ZerotoMastery..Developing.LLM.App.Frontends.with.Streamlit.rar.html No Password - Links are Interchangeable

Free Download GenAI World - LLM, Fine-tuning, RAG & Prompt engineering Published 10/2024 Created by Rabbitt Learning MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 12 Lectures ( 42m ) | Size: 561 MB The single source of truth What you'll learn: Understand the fundamentals of prompting in the context of large language models (LLMs). Learn the importance of prompt engineering for optimizing model perform Explore advanced concepts like Direct Preference Optimization (DPO) and Parameter-Efficient Fine-Tuning (PEFT). Gain insights into Retrieval Augmented Generation (RAG), understanding its components and how it enhances LLM capabilities. Requirements: Yes, students should have: A foundational understanding of artificial intelligence and machine learning concepts, especially related to language models. Proficiency in Python programming, as the course includes detailed code examples and exercises. Familiarity with deep learning frameworks. Basic knowledge of natural language processing (NLP) and transformer models. Access to necessary computational resources, such as a GPU-enabled environment. Description: This course covers everything from Large Language Models (LLMs), prompt engineering to parameter-efficient fine-tuning (PEFT) and advanced concepts like Direct Preference Optimization (DPO). You'll also dive deep into Retrieval Augmented Generation (RAG) to enhance your LLMs' capabilities by integrating retrieval systems for superior responses.By the end of this course, you'll be equipped to create AI solutions that align perfectly with human intent and outperform standard models. What You'll Learn:Craft powerful and effective prompts for LLMs to optimize outputs.Master Direct Preference Optimization (DPO) and PEFT for domain-specific fine-tuning.Implement Retrieval Augmented Generation (RAG) to elevate model performance.Gain insights into state-of-the-art LLM capabilities, focusing on practical and advanced techniques.Develop customized solutions with hands-on code examples and exercises. What you will Get A foundational understanding of artificial intelligence and machine learning concepts, especially related to language models. Proficiency in Python programming, as the course includes detailed code examples and exercises. Familiarity with deep learning frameworks. Basic knowledge of natural language processing (NLP) and transformer models. Access to necessary computational resources, such as a GPU-enabled environment. In addition to the core topics, our course also features real-world case studies on fine-tuning, prompt engineering, and Retrieval Augmented Generation (RAG). These case studies offer practical, hands-on insights into how these techniques are applied in real AI projects .These case studies provide a practical framework for applying the theoretical concepts covered in the course, helping learners implement these methods in their own projects. Who this course is for: This course is ideal for: Machine learning engineers and data scientists looking to enhance their skills in fine-tuning large language models. AI researchers and practitioners interested in advanced techniques like RAG, PEFT, and QLoRA. Developers and programmers aiming to implement AI solutions that require domain-specific model customization. Students and academics studying artificial intelligence, machine learning, or natural language processing. Anyone interested in state-of-the-art AI technologies and how to apply them effectively in real-world scenarios. Homepage https://www.udemy.com/course/genai-world-llm-fine-tuning-rag-prompt-engineering/ Rapidgator https://rg.to/file/f59fdf8623032aa4f829304a12fe9127/nonlu.GenAI.World..LLM.Finetuning.RAG..Prompt.engineering.rar.html Fikper Free Download https://fikper.com/Tb2IRzSxjq/nonlu.GenAI.World..LLM.Finetuning.RAG..Prompt.engineering.rar.html No Password - Links are Interchangeable

Free Download Non Functional Testing For Llm, Chatbots And Ai Models Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.96 GB | Duration: 5h 6m Learn essential AI testing techniques to ensure reliable, ethical, and human-like performance of advanced AI systems What you'll learn Understand how AI is working Understand basic software testing Understand how AI is tested compared to traditional software Gain knowledge on testing for ethics Demo on testing Chat GPT with automated Tools Understand Adversial Testing techniques Understand how to test for a human like conversation Create a framework for testing bias, toxicity and hate with PerspectiveAPI Requirements basic experience with software testing basic coding experience ( but not needed) optional - GPT model 4 subscription (but not needed) desire to learn the hottest skill on the market desire to learn the hottest skill on the market Description Welcome to "Non Functional Testing for LLM, Chatbots and AI Models" your comprehensive guide to mastering the fundamentals of testing AI systems. Whether you're a developer, data scientist, or AI enthusiast, this course will provide you with the knowledge and skills needed to assess, improve, and ensure the reliability, performance, safety, and ethical integrity of AI technologies.What You Will Learn:Introduction to AI Testing: Understand the critical importance of testing AI systems, addressing both technical performance and ethical considerations. Learn about the potential impacts of AI failures and how responsible testing mitigates these risks.Special Focus on Foundation Models and LLMs: Dive deep into the unique challenges of testing large language models and foundational AI systems, which are driving innovation across multiple industries.AI System Evaluations: Learn how to design and implement effective testing frameworks for AI-based systems, utilizing both manual and automated tools to improve system performance and safety.Adversarial AI Testing: Understand how to evaluate the robustness of AI models through adversarial testing techniques, assessing how well AI systems resist manipulation and errors when exposed to malicious inputs.PerspectiveAPI for Ethical and Toxicity Testing: Learn how to integrate the PerspectiveAPI and other tools to test AI systems for ethical compliance and detect harmful or toxic outputs, ensuring AI systems uphold safety and ethical standards.Humanness in AI: Explore the concept of evaluating the "humanness" of AI responses. Learn how to test whether AI systems generate outputs that are human-like, contextually aware, and empathetic in their interactions.Ethical AI: Delve into the risks associated with AI and the ethical dimensions of AI development. Learn how to test AI systems for bias, fairness, and transparency, ensuring adherence to responsible AI practices.Testing ChatGPT and Chatbots Using APIs in MLOps: Learn to test and evaluate conversational models like ChatGPT through APIs, and understand how to integrate these tests into MLOps pipelines for continuous AI improvement.Case Studies: Review real-world examples of AI testing, learning from common pitfalls and best practices used in the field to ensure AI reliability and safety.Who This Course Is For:This course is designed for individuals seeking a comprehensive understanding of the techniques and practices required for testing AI systems. Whether you are starting a career in AI, enhancing your professional skills, or interested in the technical and ethical mechanisms behind AI system reliability, this course offers valuable insights.Enroll now to start mastering the critical skill of testing AI systems, ensuring that you are equipped to contribute to the development of safe, reliable, and ethically sound AI technologies! Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 About your instructor Lecture 3 A word of introduction on Generative AI Lecture 4 History of AI Lecture 5 What you will learn in this material Section 2: Setup Environment Lecture 6 Install VS Code Lecture 7 Install NodeJS and NPM Lecture 8 Install Python Lecture 9 Install Python Dependencies - PIP Section 3: Introduction to Artificial Intelligence Lecture 10 What makes up AI Lecture 11 Where do LLMs fit into AI Lecture 12 Introduction to Natural Language Processing Lecture 13 Introduction to Machine Learning (ML) Lecture 14 Machine Learning - Supervised ML Lecture 15 Machine Learning - Unsupervised ML Lecture 16 Machine Learning - Reinforced ML Lecture 17 Neural Networks and Deep Learning Lecture 18 Importance of Training Data Lecture 19 What actually is GEN AI Section 4: Introduction to LLM Basic Testing Lecture 20 Types of Testing in Software Lecture 21 Testing Types for LLMs | Foundation Models Section 5: Automated Testing Framework with Postman and ChatGPT Lecture 22 What is a token in LLMs Lecture 23 Chat GPT-API - Create Subscription Lecture 24 Get an OPENAI API Key Lecture 25 Installing Postman and first API Test Lecture 26 API Collections and making Results Deterministic Lecture 27 Installing Newman and Running with the CLI Lecture 28 Demo - GitHub - Adding Tests in ML OPS Pipeline Section 6: Toxicity Testing Framework for LLMs Lecture 29 Perspective Service - Bias Detection Service Lecture 30 Get a Perspective API Key Lecture 31 Demo - VS Code - Call Perspective API Lecture 32 Demo - Python - Test AI Response against Perspective APIs Section 7: Adversial/Security Testing for LLMs Lecture 33 Adversial attacks for LLMS and Red Team Lecture 34 Prompt Injection Attack Lecture 35 FUZZ Testing Lecture 36 Denial of Service Attacks Lecture 37 Adversial Attack Examples Lecture 38 Poisoning attack Lecture 39 Privacy Leakage Testing Lecture 40 Evasion Attacks Section 8: Non - Functional Testing - Human in AI Lecture 41 What is non functional Testing for LLMs Lecture 42 Disclaimer on Non Functional Testing Lecture 43 Non functional Testing AI Models | LLMs - Ethical Alignment Lecture 44 Non functional Testing AI Models | LLMs - Explainability Lecture 45 Non functional Testing AI Models | LLMs - User Interaction Robustness Lecture 46 Non functional Testing AI Models | LLMs - Context Preservation Lecture 47 Non functional Testing AI Models | LLMs - Creativity and Novelty Section 9: Ethical Consideration for AI Lecture 48 Asimov's 3 Laws of Robots Lecture 49 DEMO - Why we need ethical and responsible AI Systems Lecture 50 AI and Biases Lecture 51 GEN AI and Privacy Lecture 52 GEN AI and Intellectual Property Lecture 53 Gen AI and Deep Fake Lecture 54 Hallucinations Lecture 55 OPENAI-CHAT GPT Moderation Service Lecture 56 Google Moderation Service Lecture 57 Spot a Fake - Demo Chat GPT Watermark on Dall E Citizen Developer,Software testers,Quality engineers,Social Engineers,Prompt Engineers,Product Managers,Engineering Directors Homepage https://www.udemy.com/course/non-functional-testing-for-llm-chatbots-and-ai-models/ Rapidgator https://rg.to/file/2088bf010d3b54c329a853578faf0dba/insoj.Non.Functional.Testing.For.Llm.Chatbots.And.Ai.Models.part3.rar.html https://rg.to/file/456fba635f23f81c76fcc984b03ff56c/insoj.Non.Functional.Testing.For.Llm.Chatbots.And.Ai.Models.part2.rar.html https://rg.to/file/8b2537cf65a5f3a173b5210b0f5b143f/insoj.Non.Functional.Testing.For.Llm.Chatbots.And.Ai.Models.part1.rar.html Fikper Free Download https://fikper.com/47quEgG0P0/insoj.Non.Functional.Testing.For.Llm.Chatbots.And.Ai.Models.part3.rar.html https://fikper.com/8WQv4ej80X/insoj.Non.Functional.Testing.For.Llm.Chatbots.And.Ai.Models.part1.rar.html https://fikper.com/vOfrvwtImu/insoj.Non.Functional.Testing.For.Llm.Chatbots.And.Ai.Models.part2.rar.html No Password - Links are Interchangeable

Free Download The NLP & LLM Crash Course - Build your AI Chatbot Quickly Published 10/2024 Created by Yersel Hurtado MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 23 Lectures ( 1h 42m ) | Size: 631 MB Master NLP and Large Language Models (LLM): Build and deploy your own ChatGPT-like chatbot with Python in record time. What you'll learn: Understand how NLP & LLMs and their architecture work Implement Sentiment Analysis models Implement Named Entity Recognition (NER) models Implement Question-Answering models Learn how to provision your own space with a GPU Learn how to create a chatbot interface Learn how to create your own AI chatbot from scratch in just 2 hours Learn how to use Open Source models like Llama 3.1, BERT, and others Requirements: Basic Knowlegde of Python Description: Master NLP and Large Language Models (LLM): Build and deploy your ChatGPT-like chatbot with Python in record time.Would you like to dive into artificial intelligence and create your own chatbot in just 2 hours? This is possible with our intensive course on NLP & LLM and Generative AI. We will teach you from scratch what a Large Language Model (LLM) is and how to leverage its power to develop innovative applications.What You'll Learn:Natural Language Processing (NLP) & Large Language Models (LLMs): Understand the architecture and inner workings of LLMs like GPT.Transformers Library: Harness pipelines for sentiment analysis and entity recognition-key skills in natural language processing.AutoClass Models: Get hands-on with AutoModel and AutoTokenizer to build question-answering systems.Advanced Environments: Set up GPU configurations and create authentication tokens to work with sophisticated AI models.Build a User Interface for the Chatbot: Create an intuitive chat-style interface to test your chatbot.Open Source Models: Learn how to choose the right model based on the specific task at hand.Chatbot Development: Build the chat logic, design an engaging user interface, and deploy your very own LLM-powered chatbot.What You Need:All you need is a basic knowledge of Python and a computer to start building your own chatbot. Who this course is for: Everyone with basic knowledge of Python Homepage https://www.udemy.com/course/the-nlp-llm-crash-course-build-an-ai-chatbot-in-2-hours/ Rapidgator https://rg.to/file/167b66ddf669897aee23e17d62a755e7/ntuop.The.NLP..LLM.Crash.Course..Build.your.AI.Chatbot.Quickly.rar.html Fikper Free Download https://fikper.com/cxjzaOYfsh/ntuop.The.NLP..LLM.Crash.Course..Build.your.AI.Chatbot.Quickly.rar.html No Password - Links are Interchangeable

Free Download Level up LLM applications development with LangChain and OpenAI Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Beginner + Intermediate | Genre: eLearning | Language: English + srt | Duration: 3h 52m | Size: 656 MB Dive into the world of large language models (LLMs) with a focus on integrating them into practical applications utilizing OpenAI APIs. Discover how to enhance LLMs with retrieval components, deploy interactive chat applications, and construct multi-retriever agents for advanced data handling. Join instructor Sandy Ludosky to gain the skills to create intelligent agents capable of performing complex tasks, from semantic searches to question-answering chatbots, significantly enhancing user experiences. Whether you're aiming to innovate in your current role or embark on new AI projects, this course provides the foundational knowledge and practical skills needed to harness the power of LLMs effectively. Homepage https://www.linkedin.com/learning/level-up-llm-applications-development-with-langchain-and-openai TakeFile https://takefile.link/c0x8hlei5l18/bowlt.Level.up.LLM.applications.development.with.LangChain.and.OpenAI.rar.html Rapidgator https://rg.to/file/7a56d572fa1de6b884a75d07e5eee780/bowlt.Level.up.LLM.applications.development.with.LangChain.and.OpenAI.rar.html Fikper Free Download https://fikper.com/h2kFd6M4hX/bowlt.Level.up.LLM.applications.development.with.LangChain.and.OpenAI.rar.html No Password - Links are Interchangeable

Free Download AI Agents - Building Teams of LLM Agents that Work For You Published 9/2024 Created by Mohsen Hassan,Ilyass Tabiai, PhD MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 45 Lectures ( 8h 42m ) | Size: 8.52 GB AutoGen, ChatGPT API, Streamlit, Google Cloud, everything to build and deploy AI Agents based apps (locally or at scale) What you'll learn: Build teams of AI Agents that can achieve complex tasks Build LLM Agents based Apps Use ChatGPT's API Use AutoGen to enable AI Agents to communicate with one another Build a front-end to communicate with your team of AI Agents (optional) Run a AI Agent App at scale using Google Cloud (optional) Set up a payment system to charge users to use your AI Agents based App (optional) Requirements: Basic Programming Knowledge (we explain all code provided step by step) No prior knowledge required, everything is shown step by step. Description: In this course you'll learn about this new way of using LLM Agents: deploying multiple agents to work together as teams to accomplish more complex tasks for you!Everything is taught step by step and the course is fully practical with multiple examples and one complete AI Agents-based App that we build together.One of the things we use to accomplish this is ChatGPT's API so we can use ChatGPT through Python.We also use AutoGen to enable our Agents to work together and communicate with one another (to accomplish tasks with no human intervention).We also provide a few optional sections. One of these sections teaches to have a front-end, using Streamlit, to more easily interact with your AI Agents.Another optional section is for those who want to run AI Agents at scale! Here we show you how to deploy your LLM Agents on Google Cloud, so anyone can use your product.Lastly, one more optional section is available showing how to set up a payment system/subscription model using Stripe for those who want to monetize their AI Agents-based App!Everything is explained simply and in a step-by-step approach. All code shown in the course is also provided. Who this course is for: Everyone ready to learn about this brand new way of using LLM Agents Homepage https://anonymz.com/https://www.udemy.com/course/ai-agents-building-teams-of-llm-agents-that-work-for-you/ Rapidgator https://rg.to/file/97364c36ca0b6beb1bc1eeea10481a70/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part01.rar.html https://rg.to/file/c5d806f1147816de780de13c80cd6cf0/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part02.rar.html https://rg.to/file/2738a5721bb2093529067295a07d797a/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part03.rar.html https://rg.to/file/40e63bdfbc937dfa842106f58655f1da/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part04.rar.html https://rg.to/file/6f6a70fcb62833ce31523481f6658f51/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part05.rar.html https://rg.to/file/b4a189b0ebffe0f05f0ae58a671e5115/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part06.rar.html https://rg.to/file/24eda7b6619c5acb80179ff80b90866f/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part07.rar.html https://rg.to/file/7a54d7ad9408cc856b0668191ca569bb/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part08.rar.html https://rg.to/file/8c64f713fb5f196c0dc8637a50b578d4/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part09.rar.html Fikper Free Download https://fikper.com/X4kgYMagqN/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part01.rar.html https://fikper.com/5HHRB2gjEj/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part02.rar.html https://fikper.com/RK2UPaE1IP/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part03.rar.html https://fikper.com/cf9MvNiA6b/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part04.rar.html https://fikper.com/7fmv2NixE4/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part05.rar.html https://fikper.com/e8yHfeaTam/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part06.rar.html https://fikper.com/HlPkkmpuTT/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part07.rar.html https://fikper.com/7dwIwFeIWi/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part08.rar.html https://fikper.com/Fg5mQ58W0d/zbfmo.AI.Agents.Building.Teams.of.LLM.Agents.that.Work.For.You.part09.rar.html No Password - Links are Interchangeable

Free Download Llm Engineering - Master Ai & Large Language Models (Llms) Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 5.65 GB | Duration: 7h 26m Master Generative AI and Large Language Models (LLMs). Explore and deploy LLM applications, learn fundamental theory. What you'll learn Design and develop a full solution to a given business problem by selecting, training and applying LLMs Compare and contrast the latest techniques for improving the performance of your LLM solution, such as RAG, fine-tuning and agentic workflows Weigh up the leading 10 frontier and 10 open-source LLMs, and be able to select the best choice for a given task Solve problems by applying leading open-source platforms, frameworks and tools, including Hugging Face, Gradio and Weights & Biases State the common AI paradigms, and identify the types of business problems most suitable for each Define fundamental data science concepts around deep learning, including training vs inference, generalizing vs overfitting, and the key ideas behind the NN Describe core concepts such as Generative AI, LLMs and the Transformer Architecture, and discuss what can be achieved with state-of-the-art performance Explain how LLMs work in sufficient detail to be able to train and test them, apply them to new scenarios, and diagnose & fix common issues Implement LLM solutions in Python using frontier and open-source models with both APIs and direct inference Execute code to write documents, answer questions and generate images. Requirements Familiarity with Python. This course will not cover Python basics and is completed in Python. Description Mastering Generative AI and LLMs: An 8-Week Hands-On JourneyAccelerate your career in AI with practical, real-world projects led by industry veteran Ed Donner. Build advanced Generative AI products, experiment with over 20 groundbreaking models, and master state-of-the-art techniques like RAG, QLoRA, and Agents.What you'll learn• Build advanced Generative AI products using cutting-edge models and frameworks.• Experiment with over 20 groundbreaking AI models, including Frontier and Open-Source models.• Develop proficiency with platforms like HuggingFace, LangChain, and Gradio.• Implement state-of-the-art techniques such as RAG (Retrieval-Augmented Generation), QLoRA fine-tuning, and Agents.• Create real-world AI applications, including:• A multi-modal customer support assistant that interacts with text, sound, and images.• An AI knowledge worker that can answer any question about a company based on its shared drive.• An AI programmer that optimizes software, achieving performance improvements of over 60,000 times.• An ecommerce application that accurately predicts prices of unseen products.• Transition from inference to training, fine-tuning both Frontier and Open-Source models.• Deploy AI products to production with polished user interfaces and advanced capabilities.• Level up your AI and LLM engineering skills to be at the forefront of the industry.About the InstructorI'm Ed Donner, an entrepreneur and leader in AI and technology with over 20 years of experience. I've co-founded and sold my own AI startup, started a second one, and led teams in top-tier financial institutions and startups around the world. I'm passionate about bringing others into this exciting field and helping them become experts at the forefront of the industry.Why This Course?• Hands-On Learning: The best way to learn is by doing. You'll engage in practical exercises, building real-world AI applications that deliver stunning results.• Cutting-Edge Techniques: Stay ahead of the curve by learning the latest frameworks and techniques, including RAG, QLoRA, and Agents.• Accessible Content: Designed for learners at all levels. Step-by-step instructions, practical exercises, cheat sheets, and plenty of resources are provided.• No Advanced Math Required: The course focuses on practical application. No calculus or linear algebra is needed to master LLM engineering.Course StructureWeek 1: Foundations and First Projects• Dive into the fundamentals of Transformers.• Experiment with six leading Frontier Models.• Build your first business Gen AI product that scrapes the web, makes decisions, and creates formatted sales brochures.Week 2: Frontier APIs and Customer Service Chatbots• Explore Frontier APIs and interact with three leading models.• Develop a customer service chatbot with a sharp UI that can interact with text, images, audio, and utilize tools or agents.Week 3: Embracing Open-Source Models• Discover the world of Open-Source models using HuggingFace.• Tackle 10 common Gen AI use cases, from translation to image generation.• Build a product to generate meeting minutes and action items from recordings.Week 4: LLM Selection and Code Generation• Understand the differences between LLMs and how to select the best one for your business tasks.• Use LLMs to generate code and build a product that translates code from Python to C++, achieving performance improvements of over 60,000 times.Week 5: Retrieval-Augmented Generation (RAG)• Master RAG to improve the accuracy of your solutions.• Become proficient with vector embeddings and explore vectors in popular open-source vector datastores.• Build a full business solution similar to real products on the market today.Week 6: Transitioning to Training• Move from inference to training.• Fine-tune a Frontier model to solve a real business problem.• Build your own specialized model, marking a significant milestone in your AI journey.Week 7: Advanced Training Techniques• Dive into advanced training techniques like QLoRA fine-tuning.• Train an open-source model to outperform Frontier models for specific tasks.• Tackle challenging projects that push your skills to the next level.Week 8: Deployment and Finalization• Deploy your commercial product to production with a polished UI.• Enhance capabilities using Agents.• Deliver your first productionized, agentized, fine-tuned LLM model.• Celebrate your mastery of AI and LLM engineering, ready for a new phase in your career. Overview Section 1: Week 1 - Build Your First LLM Product: Exploring Frontier Models & Transformers Lecture 1 Day 1 - Mastering LLM Engineering: From Basics to Outperforming GPT-4 in 8 Weeks Lecture 2 Day 1 - Getting Started with Generative AI: First Steps in LLM Project Setup Lecture 3 Day 1 - Building a Web Page Summarizer with OpenAI GPT-4: Instant Gratification Lecture 4 Day 1 - Mastering OpenAI API: Write Code for Frontier Models in Generative AI Lecture 5 Day 2 - Generative AI Course Structure: 8 Weeks to LLM Mastery Lecture 6 Day 2 - Exploring Frontier LLMs: ChatGPT, Claude, Gemini and more Lecture 7 Day 3 - Frontier LLMs: Exploring Strengths and Weaknesses of Top Gen AI Models Lecture 8 Day 3 - ChatGPT vs Other LLMs: Strengths, Weaknesses, and Complementary Models Lecture 9 Day 3 - Claude AI: Exploring Capabilities and Limitations of the Frontier Model Lecture 10 Day 3 - Comparing Gemini AI to Other Frontier Models: Strengths and Limitations Lecture 11 Day 3 - Comparing Frontier LLMs: Command-R Plus, Meta AI, & Perplexity AI Models Lecture 12 Day 3 - Comparing Top AI Models: GPT-4, Claude, and Gemini in Leadership Battle Lecture 13 Day 4 - AI Leadership Battle: Analyzing GPT-4, Claude-3, and Gemini-1.5 Pitches Lecture 14 Day 4 - Gen AI Breakthroughs: Transformer Models & Emergent Intelligence Lecture 15 Day 4 - Tokenization in LLMs: How GPT Processes Text for Natural Language Tasks Lecture 16 Day 4 - Understanding Context Windows: Maximizing LLM Performance and Memory Lecture 17 Day 5 - Implementing One-Shot Prompting with OpenAI for Business Applications Lecture 18 Day 5 - How to Use GPT-4 for JSON Generation in Python: AI-Powered Web Scraping Lecture 19 Day 5 - Building a Full Business Solution with Generative AI and OpenAI's API Lecture 20 Day 5 - Extending Gen AI: Multi-Shot Prompting & Translation Techniques Section 2: Week 2 - Build a Multi-Modal Chatbot: LLMs, Gradio UI, and Agents in Action Lecture 21 Day 1 - Mastering Multiple AI APIs: OpenAI, Claude, and Gemini for LLM Engineers Lecture 22 Day 1 - Streaming AI Responses: Implementing Real-Time LLM Output in Python Lecture 23 Day 1 - How to Create Adversarial AI Conversations Using OpenAI and Claude APIs Lecture 24 Day 1 - AI Tools: Exploring Transformers & Frontier LLMs for Developers Lecture 25 Day 2 - Building AI UIs with Gradio: Quick Prototyping for LLM Engineers Lecture 26 Day 2 - Gradio Tutorial: Create Interactive AI Interfaces for OpenAI GPT Models Lecture 27 Day 2 - Implementing Streaming Responses with GPT and Claude in Gradio UI Lecture 28 Day 2 - Building a Multi-Model AI Chat Interface with Gradio: GPT vs Claude Lecture 29 Day 2 - Building Advanced AI UIs: From OpenAI API to Chat Interfaces with Gradio Lecture 30 Day 3 - Building AI Chatbots: Mastering Gradio for Customer Support Assistants Lecture 31 Day 3 - Build a Conversational AI Chatbot with OpenAI & Gradio: Step-by-Step Lecture 32 Day 3 - Enhancing Chatbots with Multi-Shot Prompting and Context Enrichment Lecture 33 Day 3 - Mastering AI Tools: Empowering LLMs to Run Code on Your Machine Lecture 34 Day 4 - Using AI Tools with LLMs: Enhancing Large Language Model Capabilities Lecture 35 Day 4 - Building an AI Airline Assistant: Implementing Tools with OpenAI GPT-4 Lecture 36 Day 4 - How to Equip LLMs with Custom Tools: OpenAI Function Calling Tutorial Lecture 37 Day 4 - Mastering AI Tools: Building Advanced LLM-Powered Assistants with APIs Lecture 38 Day 5 - Multimodal AI Assistants: Integrating Image and Sound Generation Lecture 39 Day 5 - Multimodal AI: Integrating DALL-E 3 Image Generation in JupyterLab Lecture 40 Day 5 - Build a Multimodal AI Agent: Integrating Audio & Image Tools Lecture 41 Day 5 - How to Build a Multimodal AI Assistant: Integrating Tools and Agents Section 3: Week 3 - Open-Source Gen AI: Building Automated Solutions with HuggingFace Lecture 42 Day 1 - Hugging Face Tutorial: Exploring Open-Source AI Models and Datasets Lecture 43 Day 1 - Exploring HuggingFace Hub: Models, Datasets & Spaces for AI Developers Lecture 44 Day 1 - Intro to Google Colab: Cloud Jupyter Notebooks for Machine Learning Lecture 45 Day 1 - Hugging Face Integration with Google Colab: Secrets and API Keys Setup Lecture 46 Day 1 - Mastering Google Colab: Run Open-Source AI Models with Hugging Face Lecture 47 Day 2 - Hugging Face Transformers: Using Pipelines for AI Tasks in Python Lecture 48 Day 2 - Hugging Face Pipelines: Simplifying AI Tasks with Transformers Library Lecture 49 Day 2 - Mastering HuggingFace Pipelines: Efficient AI Inference for ML Tasks Lecture 50 Day 3 - Exploring Tokenizers in Open-Source AI: Llama, Phi-2, Qwen, & Starcoder Lecture 51 Day 3 - Tokenization Techniques in AI: Using AutoTokenizer with LLAMA 3.1 Model Lecture 52 Day 3 - Comparing Tokenizers: Llama, PHI-3, and QWEN2 for Open-Source AI Models Lecture 53 Day 3 - Hugging Face Tokenizers: Preparing for Advanced AI Text Generation Lecture 54 Day 4 - Hugging Face Model Class: Running Inference on Open-Source AI Models Lecture 55 Day 4 - Hugging Face Transformers: Loading & Quantizing LLMs with Bits & Bytes Lecture 56 Day 4 - Hugging Face Transformers: Generating Jokes with Open-Source AI Models Lecture 57 Day 4 - Mastering Hugging Face Transformers: Models, Pipelines, and Tokenizers Lecture 58 Day 5 - Combining Frontier & Open-Source Models for Audio-to-Text Summarization Lecture 59 Day 5 - Using Hugging Face & OpenAI for AI-Powered Meeting Minutes Generation Lecture 60 Day 5 - Build a Synthetic Test Data Generator: Open-Source AI Model for Business Aspiring AI engineers and data scientists eager to break into the field of Generative AI and LLMs.,Professionals looking to upskill and stay competitive in the rapidly evolving AI landscape.,Developers interested in building advanced AI applications with practical, hands-on experience. 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Free Download Introduction to LLM Vulnerabilities Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 1h 25m | Size: 232 MB As large language models (LLMs) revolutionize the AI landscape, it's becoming crucial to understand and address the unique security challenges they present. In this comprehensive course from Pragmatic AI Labs, instructor Alfredo Deza covers the technical knowledge and skills required to identify, mitigate, and prevent security vulnerabilities in your LLM applications. Explore common security threats, such as model theft, prompt injection, and sensitive information disclosure, and learn practical techniques to prevent attackers from exploiting vulnerabilities and compromising your systems. Discover best practices for secure plug-in design, input validation, and sanitization, as well as how to actively monitor dependencies for security updates and vulnerabilities. Along the way, Alfredo outlines strategies for protecting AI systems against unauthorized access and data breaches. By the end of the course, you'll be prepared to deploy robust, secure, and effective AI solutions. Homepage https://www.linkedin.com/learning/introduction-to-llm-vulnerabilities TakeFile https://takefile.link/u48jdbdmw9t5/ymalx.Introduction.to.LLM.Vulnerabilities.rar.html Rapidgator https://rg.to/file/7373074758ff1018232ba3106c0257ee/ymalx.Introduction.to.LLM.Vulnerabilities.rar.html Fikper Free Download https://fikper.com/axrFX9TBVM/ymalx.Introduction.to.LLM.Vulnerabilities.rar.html No Password - Links are Interchangeable