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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