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Free Download Udemy - Strategies for Parallelizing LLMs Masterclass Published: 3/2025 Created by: Paulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: All | Genre: eLearning | Language: English | Duration: 99 Lectures ( 8h 41m ) | Size: 5.2 GB Mastering LLM Parallelism: Scale Large Language Models with DeepSpeed & Multi-GPU Systems What you'll learn Understand and Apply Parallelism Strategies for LLMs Implement Distributed Training with DeepSpeed Deploy and Manage LLMs on Multi-GPU Systems Enhance Fault Tolerance and Scalability in LLM Training Requirements Basic knowledge of Python programming and deep learning concepts. Familiarity with PyTorch or similar frameworks is helpful but not required. Access to a GPU-enabled environment (e.g., colab) for hands-on sections-don't worry, we'll guide you through setup! Description Mastering LLM Parallelism: Scale Large Language Models with DeepSpeed & Multi-GPU SystemsAre you ready to unlock the full potential of large language models (LLMs) and train them at scale? In this comprehensive course, you'll dive deep into the world of parallelism strategies, learning how to efficiently train massive LLMs using cutting-edge techniques like data, model, pipeline, and tensor parallelism. Whether you're a machine learning engineer, data scientist, or AI enthusiast, this course will equip you with the skills to harness multi-GPU systems and optimize LLM training with DeepSpeed.What You'll LearnFoundational Knowledge: Start with the essentials of IT concepts, GPU architecture, deep learning, and LLMs (Sections 3-7). Understand the fundamentals of parallel computing and why parallelism is critical for training large-scale models (Section 8).Types of Parallelism: Explore the core parallelism strategies for LLMs-data, model, pipeline, and tensor parallelism (Sections 9-11). Learn the theory and practical applications of each method to scale your models effectively.Hands-On Implementation: Get hands-on with DeepSpeed, a leading framework for distributed training. Implement data parallelism on the WikiText dataset and master pipeline parallelism strategies (Sections 12-13). Deploy your models on RunPod, a multi-GPU cloud platform, and see parallelism in action (Section 14).Fault Tolerance & Scalability: Discover strategies to ensure fault tolerance and scalability in distributed LLM training, including advanced checkpointing techniques (Section 15).Advanced Topics & Trends: Stay ahead of the curve with emerging trends and advanced topics in LLM parallelism, preparing you for the future of AI (Section 16).Why Take This Course?Practical, Hands-On Focus: Build real-world skills by implementing parallelism strategies with DeepSpeed and deploying on Run Pod's multi-GPU systems.Comprehensive Deep Dives: Each section includes in-depth explanations and practical examples, ensuring you understand both the "why" and the "how" of LLM parallelism.Scalable Solutions: Learn techniques to train LLMs efficiently, whether you're working with a single GPU or a distributed cluster.Who this course is for Machine learning engineers and data scientists looking to scale LLM training.AI researchers interested in distributed computing and parallelism strategies.Developers and engineers working with multi-GPU systems who want to optimize LLM performance.Anyone with a basic understanding of deep learning and Python who wants to master advanced LLM training techniques.PrerequisitesBasic knowledge of Python programming and deep learning concepts.Familiarity with PyTorch or similar frameworks is helpful but not required.Access to a GPU-enabled environment (e.g., run pod) for hands-on sections-don't worry, we'll guide you through setup! Who this course is for Machine learning engineers and data scientists looking to scale LLM training. AI researchers interested in distributed computing and parallelism strategies. Developers and engineers working with multi-GPU systems who want to optimize LLM performance. Anyone with a basic understanding of deep learning and Python who wants to master advanced LLM training techniques. 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Released 3/2025 MP4 | Free Download Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 32 Lessons ( 4h 55m ) | Size: 688 MB Dive deep into the mathematics powering transformers like GPT and BERT. Master attention mechanisms, positional encodings, and embeddings to understand the tech behind cutting-edge AI and language models. What you'll learn How tokenization transforms text into model-readable data The inner workings of attention mechanisms in transformers How positional encodings preserve sequence data in AI models The role of matrices in encoding and processing language Building dense word representations with multi-dimensional embeddings Differences between bidirectional and masked language models Practical applications of dot products and vector mathematics in AI How transformers process, understand, and generate human-like text What Are Transformers? So many millennia ago the AutoBots and Decepticons fought over Cybertron... Oh wait, sorry. Wrong Transformers. The Transformer architecture is a foundational model in modern artificial intelligence, particularly in natural language processing (NLP). Introduced in the seminal paper "Attention Is All You Need" by Vaswani et al. in 2017, it is one of the most important technological breakthroughs that gave rise to the Large Language Models you know today like ChatGPT and Claude. What makes Transformers special is that instead of reading word-by-word like old systems (called recurrent models), the Transformer looks at the whole sentence all at once. It uses something called attention to figure out which words are important to focus on for each task. For example, if you're translating "She opened the box because it was her birthday," the word "it" might need special attention to understand it refers to "the box." Why Learn The Transformer Architecture? 1. They Power Modern AI Applications Transformers are the backbone of many AI systems today. Models like GPT, BERT (used in search engines like Google), and DALL·E (image generation) are all based on Transformers. If you're interested in these technologies, understanding Transformers gives you insight into how they work. 2. They Represent AI's Cutting Edge Transformers revolutionized AI, shifting from older methods like RNNs (Recurrent Neural Networks) to a whole new way of processing information. Learning them helps you understand why this shift happened and how it unlocked a new level of AI capability. 3. They're Widely Used in Research and Industry Whether you want to work in academia, build AI products, or explore mechanistic interpretability (which you've expressed interest in), Transformers are often the core technology. Understanding them can open doors to exciting projects and careers. 6. They're Fun and Intellectually Challenging The concept of self-attention and how Transformers handle context is elegant and powerful. Learning about them can feel like solving a fascinating puzzle. It's rewarding to see how they "think" and to realize why they're so effective. Why This Transformers Course? Well, because it teaches you advanced, dense material in a clear and enjoyable way - which is no easy feat! But of course we're biased. So here's a breakdown of what's covered in this Advanced AI course so that you can make up your own mind Introduction to Tokenization Learn how transformers convert raw text into a processable format using techniques like the WordPiece algorithm. Discover the importance of tokenization in enabling language understanding. Foundations of Transformer Architectures Understand the roles of key, query, and value matrices in encoding information and facilitating the flow of data through a model. Mechanics of Attention Mechanisms Dive into multi-head attention, attention masks, and how they allow models to focus on relevant data for better context comprehension. Positional Encodings Explore how models maintain the sequence of words in inputs using cosine and sine functions for embedding positional data. Bidirectional and Masked Language Models Study the distinctions and applications of bidirectional transformers and masked models in language tasks. Vector Mathematics and Embeddings Master vectors, dot products, and multi-dimensional embeddings to create dense word representations critical for AI tasks. Applications of Attention and Encoding Learn how attention mechanisms and positional encoding come together to process and generate coherent text. Capstone Knowledge for AI Innovation Consolidate your understanding of transformer algorithms to develop and innovate with state-of-the-art AI tools. 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Free Download Udemy - Mathematics For Machine Learning And Llms Published: 3/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 2.81 GB | Duration: 15h 28m How is math used in AI What you'll learn Machine Learning mathematics linear algebra, statistics, probability and calculus for machine learning How algorithms works How algorithms are parametrizided Requirements Basic notions of machine learning Description Machine Learning is one of the hottest technologies of our time! If you are new to ML and want to become a Data Scientist, you need to understand the mathematics behind ML algorithms. There is no way around it. It is an intrinsic part of the role of a Data Scientist and any recruiter or experienced professional will attest to that. The enthusiast who is interested in learning more about the magic behind Machine Learning algorithms currently faces a daunting set of prerequisites: Programming, Large Scale Data Analysis, mathematical structures associated with models and knowledge of the application itself. A common complaint of mathematics students around the world is that the topics covered seem to have little relevance to practical problems. But that is not the case with Machine Learning.This course is not designed to make you a Mathematician, but it does provide a practical approach to working with data and focuses on the key mathematical concepts that you will encounter in machine learning studies. It is designed to fill in the gaps for students who have missed these key concepts as part of their formal education, or who need to catch up after a long break from studying mathematics.Upon completing the course, students will be equipped to understand and apply mathematical concepts to analyze and develop machine learning models, including Large Language Models. Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 The Learning Diagram Lecture 3 Python Section 2: Types of Learning Lecture 4 Supervised Learnimg Lecture 5 Unsupervised Learning Lecture 6 Reinforcement Learning Lecture 7 When to Use and Not to Use ML Lecture 8 How to chose ML Algorithms Section 3: Data Preparation Lecture 9 Preeliminar Analysis Lecture 10 The Target Variable Lecture 11 Missing Data Lecture 12 Log Transformation - Homocedasticity Lecture 13 Outliers and Anomaly Detection Lecture 14 Data Transformation Lecture 15 Data Transformation (cont.) Section 4: Statistics in the Context off ML Lecture 16 Significant Differences Lecture 17 Descriptive and Inferential Statistics Section 5: Descriptive Statistics Lecture 18 Variables and Metrics Lecture 19 Correlation and Covariance Section 6: Probabilities for ML Lecture 20 Uncertainity Lecture 21 Frquentist versus Bayesian Probabilities Lecture 22 Random Variables and Sampling Lecture 23 Sampling Spaces Lecture 24 Basic Definitions of Probabilities Lecture 25 Axions, Theorems, Independence Lecture 26 Conditional Probability Lecture 27 Bayes Theorem and Naive Bayes Algorithm Lecture 28 Expectation, Chance and Likelihood Lecture 29 Maximum Likelihood Estimation (MLE) Lecture 30 Simulations Lecture 31 Monte Carlo Simulation, Markov Chainn Lecture 32 Probability Distributions Lecture 33 Families of Distributions Lecture 34 Normal Distribution Lecture 35 Tests for Normality Lecture 36 Exponential Distribution Lecture 37 Weibull Distribution and Survival Analysis Lecture 38 Binomial Distribution Lecture 39 Poisson Distribution Section 7: Statiscs Tests Lecture 40 Hypothesis Testing Lecture 41 The p- value Lecture 42 Critical Value, Significance, Confidence, CLT, LLN Lecture 43 Z and T Tests Lecture 44 Degrees of Freedom and F statistics Lecture 45 ANOVA Lecture 46 Chi Squared Test Lecture 47 Statistical Power Lecture 48 Robustness and Statistical Sufficiency Section 8: Time Series Lecture 49 Times Series Decommposition Lecture 50 Autoregressive Models Lecture 51 Arima Section 9: Linear ad Non Linear Models Lecture 52 Linear and Non Linear Models Section 10: Linear Algebra for ML Lecture 53 Introduction to Linear Algebra Lecture 54 Types of Matrices Lecture 55 Matrices Operations Lecture 56 Linear Transformations Lecture 57 Matrix Decomposition and Tensors Section 11: Calculus for ML Lecture 58 Functions Lecture 59 Limits Lecture 60 The Derivative Lecture 61 Calculating the Derivative Lecture 62 Maximum and Minimum Lecture 63 Analitical vs Numerical Solutions Lecture 64 Numerical and Analytic Solution Lecture 65 Gradient Descent Section 12: Distances, Similarities, knn and k means Lecture 66 Distance Measurements Lecture 67 Similarities Lecture 68 Knn and K means Lecture 69 Distances in Python Section 13: Training, Testing ,Validation Lecture 70 Training, Testin, Validation Lecture 71 Training, Testing, Validation (cont) Section 14: The Cost Function Lecture 72 The Cost Function Lecture 73 Cost Function for Regression and Classification Lecture 74 Minimazing the Cost Function with Gradient Descent Lecture 75 Batch annd Stochastic Gradient Descent Section 15: Bias and Variance Lecture 76 Bias and Variance Introduction Lecture 77 Complexity Lecture 78 Regularization Lecture 79 Regularization (Cont) Section 16: Parametric andd Non Parametric Algorithms Lecture 80 Parametric and Non Parametric Algorithms Section 17: Learning Curves Lecture 81 Learning Curves Lecture 82 Learning Curves in Python Section 18: Dimensionality Reduction Lecture 83 PCA and SCD Lecture 84 Eigenvectors and Eigenvalues Lecture 85 Dimensionality Reduction in Python Section 19: Entropy and Information Gain Lecture 86 Entropy and Information Gain Lecture 87 Entropy and Information Gain (cont) Section 20: Linear Regression Lecture 88 Linear Regression Lecture 89 Linear Regression (cont) Lecture 90 Polinomial Regression Section 21: Classification Lecture 91 Logistic Function Lecture 92 Generalized Linear Models (GLM) Lecture 93 Decision Boundaries Lecture 94 Confusion Matrix Lecture 95 ROC and AUC Lecture 96 Visualization of Class Distribution Lecture 97 Precision and Recall Section 22: Decision Trees Lecture 98 Introduction to Decision Trees Lecture 99 Gini Index Lecture 100 Hyperparameters Lecture 101 Decision Trees in Python Section 23: Suport Vector Machines Lecture 102 Introduction to SVMs Lecture 103 Introduction to SVMs (cont) Lecture 104 Mathematics of SVMs Lecture 105 SVM in Python Section 24: Ensemble Algorithms Lecture 106 Wisdom of the Crowds Lecture 107 Bagging and Random Forest Lecture 108 Adaboost, Gradient Boosting, XGBoosting Section 25: Natural Language Processing Lecture 109 Introduction to NLP Lecture 110 Tokenization and Embeddings Lecture 111 Weights and Representation Lecture 112 Sequences and Sentiment Analysis Section 26: Neural Networks Lecture 113 Mathematical Model of Artificial Neuron Lecture 114 Activation Functions Lecture 115 Activation Functions (cont) Lecture 116 Weights and Bias Parameters Lecture 117 Feedforward and Backpropagation Concepts Lecture 118 Feedforward Process Lecture 119 Backpropagation Process Lecture 120 Recurent Neural Networks (RNN) Lecture 121 Convolution Neural Networks (CNN) Lecture 122 Convolution Neural Networks (CNN) (cont) Lecture 123 Seq2Seq and Aplications of NN Section 27: Large Language Models Lecture 124 Generative vs Descriptive AI Lecture 125 LLMs Properties Section 28: Transformers Lecture 126 Introduction to Transformers Lecture 127 Training and Inference Lecture 128 Basic Arquitecture of Transformers Lecture 129 Encoder Workflow Lecture 130 Sel Attention Lecture 131 Multi-Head Attention Lecture 132 Normalization and Residual Connections Lecture 133 Decoder Lecture 134 Types of Transformers Arquitecture Data Scientists and AI professionals Homepage: https://www.udemy.com/course/mathematics-for-machine-learning-and-llms/ DOWNLOAD NOW: Udemy - Mathematics For Machine Learning And Llms Rapidgator https://rg.to/file/83fa111daccde3a58d87ef278952048e/bcmff.Mathematics.For.Machine.Learning.And.Llms.part1.rar.html https://rg.to/file/9a9ecbca8ff6722e7ad89b8d169f7897/bcmff.Mathematics.For.Machine.Learning.And.Llms.part3.rar.html https://rg.to/file/ab651b47d1faa554346a88ca8d4ed50a/bcmff.Mathematics.For.Machine.Learning.And.Llms.part2.rar.html Fikper Free Download https://fikper.com/2EnkhSDlAP/bcmff.Mathematics.For.Machine.Learning.And.Llms.part1.rar.html https://fikper.com/jhjLb2L2M5/bcmff.Mathematics.For.Machine.Learning.And.Llms.part2.rar.html 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Free Download Linkedin - Introduction to NLP and LLMs Principles and Practical Applications Released 02/2025 With Gwendolyn Stripling MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Beginner | Genre: eLearning | Language: English + subtitle | Duration: 1h 39m 13s | Size: 141 MB Gain a practical understanding of natural language processing (NLP) and large language models (LLMs), unlocking the exciting possibilities of these transformative technologies. Course details Get up to speed with two of the most talked-about fields in tech today: natural language processing (NLP) and large language models (LLMs). In this course, designed uniquely for nontechnical and nonprogramming professionals, instructor Gwendolyn Stripling provides a comprehensive Overview of how computers process and understand human language. Explore real-world applications of NLP and LLMs across various industries, from chatbots and virtual assistants to sentiment analysis and content generation. Along the way, discover some of the potential impacts of these powerful technologies while developing a critical perspective of their benefits, costs, and challenges. Homepage: https://www.linkedin.com/learning/introduction-to-nlp-and-llms-principles-and-practical-applications DOWNLOAD NOW: Linkedin - Introduction to NLP and LLMs Principles and Practical Applications Fileaxa https://fileaxa.com/bumo19ivpa5b/efjod.Introduction.to.NLP.and.LLMs.Principles.and.Practical.Applications.rar TakeFile https://takefile.link/bw8dfc3kcuvn/efjod.Introduction.to.NLP.and.LLMs.Principles.and.Practical.Applications.rar.html Rapidgator https://rg.to/file/9f4e5ef060f6783b43d921e5fae9d8d3/efjod.Introduction.to.NLP.and.LLMs.Principles.and.Practical.Applications.rar.html Fikper Free Download https://fikper.com/gEz0NDjQb6/efjod.Introduction.to.NLP.and.LLMs.Principles.and.Practical.Applications.rar.html : No Password - Links are Interchangeable

Free Download LLMs in Production, Video Edition By Christopher Brousseau, Matthew Sharp Released 1/2025 By Christopher Brousseau, Matthew Sharp MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 14h 25m | Size: 2.57 GB Learn how to put Large Language Model-based applications into production safely and efficiently. This practical book offers clear, example-rich explanations of how LLMs work, how you can interact with them, and how to integrate LLMs into your own applications. Find out what makes LLMs so different from traditional software and ML, discover best practices for working with them out of the lab, and dodge common pitfalls with experienced advice. In LLMs in Production you will Grasp the fundamentals of LLMs and the technology behind them Evaluate when to use a premade LLM and when to build your own Efficiently scale up an ML platform to handle the needs of LLMs Train LLM foundation models and finetune an existing LLM Deploy LLMs to the cloud and edge devices using complex architectures like PEFT and LoRA Build applications leveraging the strengths of LLMs while mitigating their weaknesses LLMs in Production delivers vital insights into delivering MLOps so you can easily and seamlessly guide one to production usage. Inside, you'll find practical insights into everything from acquiring an LLM-suitable training dataset, building a platform, and compensating for their immense size. Plus, tips and tricks for prompt engineering, retraining and load testing, handling costs, and ensuring security. About the Technology Most business software is developed and improved iteratively, and can change significantly even after deployment. By contrast, because LLMs are expensive to create and difficult to modify, they require meticulous upfront planning, exacting data standards, and carefully-executed technical implementation. Integrating LLMs into production products impacts every aspect of your operations plan, including the application lifecycle, data pipeline, compute cost, security, and more. Get it wrong, and you may have a costly failure on your hands. About the Book LLMs in Production teaches you how to develop an LLMOps plan that can take an AI app smoothly from design to delivery. You'll learn techniques for preparing an LLM dataset, cost-efficient training hacks like LORA and RLHF, and industry benchmarks for model evaluation. Along the way, you'll put your new skills to use in three exciting example projects: creating and training a custom LLM, building a VSCode AI coding extension, and deploying a small model to a Raspberry Pi. What's Inside Balancing cost and performance Retraining and load testing Optimizing models for commodity hardware Deploying on a Kubernetes cluster About the Reader For data scientists and ML engineers who know Python and the basics of cloud deployment. About the Authors Christopher Brousseau and Matt Sharp are experienced engineers who have led numerous successful large scale LLM deployments. https://www.oreilly.com/library/view/llms-in-production/9781633437203VE/ Fileaxa https://fileaxa.com/04alm5754pqo/ogwzb.LLMs.in.Production.Video.Edition.part1.rar https://fileaxa.com/mw79h2nqqai5/ogwzb.LLMs.in.Production.Video.Edition.part2.rar https://fileaxa.com/k3ljmimbj31y/ogwzb.LLMs.in.Production.Video.Edition.part3.rar TakeFile https://takefile.link/telvt1a4clml/ogwzb.LLMs.in.Production.Video.Edition.part1.rar.html https://takefile.link/sta36pfgu63q/ogwzb.LLMs.in.Production.Video.Edition.part2.rar.html https://takefile.link/cev2dgad9c74/ogwzb.LLMs.in.Production.Video.Edition.part3.rar.html Rapidgator https://rg.to/file/34080abe62ce4c20a78f4d10a70e9851/ogwzb.LLMs.in.Production.Video.Edition.part1.rar.html https://rg.to/file/92be4f94a6852ae193876613b6e25265/ogwzb.LLMs.in.Production.Video.Edition.part2.rar.html https://rg.to/file/0042a0a2ced77b7dddfcf65e05e55e95/ogwzb.LLMs.in.Production.Video.Edition.part3.rar.html Fikper Free Download https://fikper.com/X8tfQvFeQW/ogwzb.LLMs.in.Production.Video.Edition.part1.rar.html https://fikper.com/cPWCnm0Sxg/ogwzb.LLMs.in.Production.Video.Edition.part2.rar.html https://fikper.com/upnu476Kh6/ogwzb.LLMs.in.Production.Video.Edition.part3.rar.html : No Password - Links are Interchangeable

Free Download Ollama Tutorial for Beginners - Run LLMs locally with ease Published 10/2024 Created by Studyopedia Trainings MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 11 Lectures ( 30m ) | Size: 257 MB Learn how to use Ollama to work with LLMs. Also, create a ChatGPT-like model locally with Ollama. What you'll learn Learn what is Ollama Work with different LLMs using Ollama locally Create a custom ChatGPT-like model with Ollama Learn all the Ollama commands Customize a model locally Requirements Knowledge of internet and web browser Description Welcome to the Ollama Course by Studyopedia !!!Ollama is an open-source platform to download, install, manage, run, and deploy large language models (LLMs). All this can be done locally with Ollama. LLM stands for Large Language Model. These models are designed to understand, generate, and interpret human language at a high level. FeaturesModel Library: Offers a variety of pre-built models like Llama 3.2, Mistral, etc.Customization: Allows you to customize and create your own modelsEasy: Provides a simple API for creating, running, and managing modelsCross-Platform: Available for macOS, Linux, and WindowsModelfile: Packages everything you need to run an LLM into a single Modelfile, making it easy to manage and run modelsPopular LLMs, such as Llama by Meta, Mistral, Gemma by Google's DeepMind, Phi by Microsoft, Qwen by Alibaba Clouse, etc., can run locally using Ollama.In this course, you will learn about Ollama and how it eases the work of a programmer running LLMs. We have discussed how to begin with Ollama, install, and tune LLMs like Lama 3.2, Mistral 7b, etc. We have also covered how to customize a model and create a teaching assistant like ChatBot locally by creating a modefile.**Lessons covered**Ollama - Introduction and FeaturesInstall Ollama Windows 11 locallyInstall Llama 3.2 Windows 11 locallyInstall Mistral 7b on Windows 11 locallyList all the models running on Ollama locallyList the models installed on your system with OllamaShow the information of a model using Ollama locallyHow to stop a running model on OllamaHow to run an already installed model on Ollama locallyCreate a custom GPT or customize a model with OllamaRemove any model from Ollama locallyNote: We have covered only open-source technologiesLet's start the journey! Who this course is for Beginner Machine Learning Developers Those who want to create a model Those who want to run LLMs locally Homepage https://www.udemy.com/course/ollama-tutorial/ Screenshot Rapidgator https://rg.to/file/da6f17e44ef0ef6fdce1f96a41280e95/mghdg.Ollama.Tutorial.for.Beginners..Run.LLMs.locally.with.ease.rar.html Fikper Free Download https://fikper.com/nU2BC7GVIe/mghdg.Ollama.Tutorial.for.Beginners..Run.LLMs.locally.with.ease.rar.html No Password - Links are Interchangeable

Free Download Mastering Local LLMs with Ollama and Python + Doing Projects Last updated 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 5h 40m | Size: 5.15 GB Hands-On Projects with Ollama, Langchain, CrewAI, and HuggingFace to Enhance Your AI Skills and Transform Everyday Tasks What you'll learn Understanding LLMs: Gain a solid foundation in Large Language Models (LLMs) and their applications. Using Ollama: Learn how to utilize the Ollama library for various NLP tasks. LangChain Integration: Master the integration of LangChain for building complex applications with LLMs. Project Development: Develop practical projects that reinforce learning. Creating a Learning Python Tool with Ollama. Building a Video Describer that summarizes video content. Implementing a Chat with PDF feature using Ollama LLM. Developing a Chat with VIDEO application using Ollama LLM and Whisper for audio transcription. Designing a system to Get Model Answers based on long stories or texts. Creating a Chat with Your Note application to interact with personal notes. Building a Chat with Your Diary project to reflect on personal experiences and insights. Whisper Integration: Understand how to integrate Whisper for audio processing and transcription tasks. Hugging Face Models: Learn how to leverage Hugging Face models for text generation and other NLP tasks. Practical Skills: Acquire hands-on experience through coding exercises and project implementations. Problem-Solving Techniques: Develop skills to tackle real-world problems using LLMs and related technologies. Additional Benefits: Continuous updates with new projects to enhance learning and keep up with advancements in AI and NLP technologies. Requirements Basic Knowledge of Python Description Welcome! This comprehensive course is designed for individuals eager to dive into the world of Large Language Models (LLMs) and harness their power to create innovative applications that can simplify tasks in everyday life.Course OverviewIn this course, you will learn how to effectively utilize various libraries and frameworks, including Ollama, LangChain, CrewAI, and Hugging Face, to build practical projects that demonstrate the capabilities of LLMs. Through hands-on projects, you will gain a deep understanding of how these technologies work together to enhance productivity and creativity.What You Will LearnUnderstanding LLMs: Gain insights into the architecture and functioning of Large Language Models, including their applications in natural language processing (NLP).Ollama and LangChain: Learn how to leverage Ollama for efficient model deployment and LangChain for building complex applications that integrate multiple components seamlessly.Hugging Face Transformers: Explore the Hugging Face library to access a wide range of pre-trained models for various NLP tasks.Practical Applications: Implement real-world projects that showcase the power of LLMs in different contexts.Project HighlightsLearning Python Tool with Ollama: Create an interactive tool that helps users learn Python programming through guided exercises and instant feedback using an LLM.Make a Video Describer: Develop an application that generates descriptive text for video content, enhancing accessibility and understanding for users.Chat with PDF using Ollama LLM: Build a chat interface that allows users to ask questions about the content of PDF documents, providing instant answers powered by an LLM.Chat with VIDEO using Ollama LLM and Whisper: Combine video processing with speech recognition to create an application where users can interact with video content through natural language queries.Get Model Answers Based on Long Stories: Design a system that allows users to input long narratives or stories and receive concise answers or summaries from the model.Chat with Your Note: Create a personal note-taking application where users can interact with their notes using natural language queries, making information retrieval seamless.Chat with Your Diary: Develop a diary application that allows users to reflect on their entries and ask questions about their past experiences, promoting self-reflection and personal growth.Continuous LearningThis course is designed to be dynamic, with new projects added regularly to keep pace with advancements in technology and user needs. You will have the opportunity to explore new ideas and implement them in your projects, ensuring you stay ahead in the rapidly evolving field of AI and NLP.Who Should EnrollThis course is ideal for:Developers looking to expand their skill set in AI and machine learning.Data scientists interested in applying NLP techniques using state-of-the-art models.Anyone passionate about leveraging LLMs to create innovative applications that enhance productivity.Join us on this exciting journey as we explore the potential of Large Language Models through practical projects. By the end of this course, you will have the skills and knowledge needed to build your own applications using Ollama, LangChain, CrewAI, and Hugging Face, empowering you to make your life easier through technology. Enroll today and start mastering LLMs! Who this course is for Aspiring AI Developers: Individuals looking to build applications using LLMs. Data Scientists: Professionals wanting to enhance their data analysis skills with AI tools. Python Programmers: Developers interested in integrating LLMs into their Python projects. Students: Learners seeking practical experience with modern AI libraries and frameworks. AI Enthusiasts: Anyone passionate about exploring the capabilities of LLMs and their applications. Content Creators: Writers and creators wanting to leverage AI for content generation and enhancement. Educators: Teachers aiming to incorporate AI tools into their curriculum or projects. Homepage https://www.udemy.com/course/mastering-local-llms-with-ollama-and-python-doing-projects/ Screenshot Rapidgator https://rg.to/file/717caee5b1072299daf0317c83b6d9ca/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part3.rar.html https://rg.to/file/854e5288d516fc572d1c000a93f347f1/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part6.rar.html https://rg.to/file/8f5fa669b9824bf1fd5a14e6f4660d1c/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part4.rar.html https://rg.to/file/a6d3a73d2afd595967964c1f0beb0d9f/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part1.rar.html https://rg.to/file/cde03b5566796b69465bca2f540a9f57/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part5.rar.html https://rg.to/file/fe86a8fd6faa9adebc27bdd5344ebf28/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part2.rar.html Fikper Free Download https://fikper.com/3QR9Cq6iW6/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part2.rar.html https://fikper.com/AGEVkmuU6L/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part4.rar.html https://fikper.com/YF0PO36c9u/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part6.rar.html https://fikper.com/ajMS8asgng/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part3.rar.html https://fikper.com/mxtLHi1HCh/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part5.rar.html https://fikper.com/wuJXp3h6Ng/txxdv.Mastering.Local.LLMs.with.Ollama.and.Python..Doing.Projects.part1.rar.html No Password - Links are Interchangeable

Free Download Fine-Tuning LLMs for Cybersecurity Mistral, Llama, AutoTrain, and AutoGen Released 10/2024 With Akhil Sharma MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 2h 52m 10s | Size: 488 MB Explore the intersection of cybersecurity and large language models (LLMs). Learn how to leverage LLMs for open-source intelligence (OSINT), vulnerability scanning, and more. Course details Explore the emergent field of cybersecurity enhanced by large language models (LLMs) in this detailed and interactive course. Instructor Akhil Sharma starts with the basics, including the world of open-source LLMs, their architecture and importance, and how they differ from closed-source models. Learn how to run and fine-tune models to tackle cybersecurity challenges more effectively. Gather insights for identifying new threats, generating synthetic data, performing open-source intelligence (OSINT), and scanning code vulnerabilities with hands-on examples and guided challenges. Perfect for cybersecurity professionals, IT specialists, and anyone keen on understanding how AI can bolster security protocols, this course prepares you to embrace the synergy of AI for cybersecurity, unlocking new potentials in threat detection, prevention, and response. Homepage https://www.linkedin.com/learning/fine-tuning-llms-for-cybersecurity-mistral-llama-autotrain-and-autogen Screenshot Rapidgator https://rg.to/file/c9c56d72a69a0367c9ce262c1daeb6c7/qtxpl.FineTuning.LLMs.for.Cybersecurity.Mistral.Llama.AutoTrain.and.AutoGen.rar.html Fikper Free Download https://fikper.com/p4UyExO90T/qtxpl.FineTuning.LLMs.for.Cybersecurity.Mistral.Llama.AutoTrain.and.AutoGen.rar.html No Password - Links are Interchangeable

Free Download How LLMs Understand & Generate Human Language Released: 9/2024 Duration: 1h 54m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 372 MB Genre: eLearning | Language: English Your introduction to how generative large language models work. Overview Generative language models, such as ChatGPT and Microsoft Bing, are becoming a daily tool for a lot of us, but these models remain black boxes to many. How does ChatGPT know which word to output next? How does it understand the meaning of the text you prompt it with? Everyone, from those who have never once interacted with a chatbot, to those who do so regularly, can benefit from a basic understanding of how these language models function. This course answers some of your fundamental questions about how generative AI works. In this course, you learn about word embeddings: not only how they are used in these models, but also how they can be leveraged to parse large amounts of textual information utilizing concepts such as vector storage and retrieval augmented generation. It is important to understand how these models work, so you know both what they are capable of and where their limitations lie. About the Instructor Kate Harwood is part of the Research and Development team at the New York Times, researching the integration of state-of-the-art large language models into the Times' reporting and products. She also teaches introduction to AI courses through The Coding School. She has a MS in computer science from Columbia University. Her primary focus is on natural language processing and ethical AI. Learn How To Understand how human language is translated into the math that models understand Understand how generative language models choose what words to output Understand why some prompting strategies and tasks with LLMs work better than others Understand what word embeddings are and how they are used to power LLMs Understand what vector storage/retrieval augmented generation is and why it is important Critically examine the results you get from large language models Who Should Take This Course Anyone who Is interested in demystifying generative language models Wants to be able to talk about these models with peers in an informed way Wants to unveil some of the mystery inside LLMs' black boxes but does not have the time to dive deep into hands-on learning Has a potential use case for ChatGPT or other text-based generative AI or embedding storage methods in their work Rapidgator https://rg.to/file/2c1c9b2ea7b934b80ed7931c474de8dd/bfhsk.How.LLMs.Understand..Generate.Human.Language.rar.html Fikper Free Download https://fikper.com/SBfvIjO7T0/bfhsk.How.LLMs.Understand..Generate.Human.Language.rar.html No Password - Links are Interchangeable

Free Download Ai For Beginners - Master Llms And Learn Top Prompting Published 9/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 1.90 GB | Duration: 1h 36m Learn AI fundamentals, master top language models, develop expert prompting skills, and apply AI in daily life/business. What you'll learn Define and explain key AI concepts including generative AI, large language models (LLMs), and tokenization in simple terms. Compare and evaluate major AI models such as ChatGPT, Claude, Gemini, Meta's AI, and Perplexity for various applications. Develop effective AI prompting techniques using advanced frameworks to optimize interactions with language models. Apply AI tools and technologies to real-world scenarios, enhancing productivity and problem-solving in daily life and business contexts. Requirements No fancy tech skills needed here! This course is all about jumping into AI with both feet, no matter where you're starting from. The only real requirements? A dash of curiosity, a sprinkle of creativity, and an open mind ready to soak up some cool new ideas. If you can think outside the box and aren't afraid to let your imagination run wild, you're all set! Don't worry about having any special equipment or software - just bring yourself and your enthusiasm. We'll take care of the rest and guide you through this exciting AI journey. Description Curious about AI but unsure where to start? This course breaks down artificial intelligence into clear and easy-to-understand lessons. Learn to use AI confidently, whether you are a complete beginner or looking to expand your knowledge.In this course, you will learn:The fundamentals of AI and how it's changing the way we work and liveAn in-depth look at popular AI models like ChatGPT, Claude, Meta AI, Google's Gemini, and PerplexityOur exclusive TOP prompting framework to communicate effectively with AIPractical ways to integrate AI into your daily personal life and professional tasksWe break down complex concepts into simple lessons, focusing on real-world applications. You will gain hands-on experience with AI tools, learning how to craft prompts that yield impressive results.By the end of this course, you will have the confidence to:Navigate the AI landscape with easeUse AI to boost your productivity and creativityApply AI solutions to various personal and business challengesStay ahead of the curve in the rapidly evolving field of AIJoin this exciting journey and transform your understanding of AI. No technical background required ~ just bring your curiosity and willingness to learn. Let's explore the future of technology together! Overview Section 1: Before We Begin Lecture 1 Course Expectations Lecture 2 Prerequisites Section 2: Introduction to AI Lecture 3 AI Explained and a Brief History Lecture 4 Gen-AI vs. Traditional Search Lecture 5 AI Terminology Lecture 6 Model-Specific Terms Section 3: Review of Popular Models Lecture 7 A Note on Models Lecture 8 ChatGPT Lecture 9 Claude Lecture 10 Meta AI Lecture 11 Gemini Lecture 12 BONUS: Perplexity Section 4: Prompting Techniques Lecture 13 Prompting Overview Lecture 14 Prompting Techniques Lecture 15 TOP Prompting Framework Lecture 16 Live Example Lecture 17 More Tips & Advice Section 5: Use Cases Lecture 18 Use Case Overview Lecture 19 Simplifying Ideas Lecture 20 Idea Generation and Brainstorming Lecture 21 Constructive Criticism and Feedback Lecture 22 Role Playing Lecture 23 Health - Exercise & Nutrition Lecture 24 Content Summarization Lecture 25 Business Analysis Section 6: Final Thoughts Lecture 26 The AI Mindset Lecture 27 Slow AI Lecture 28 Don't Give Up Lecture 29 Final Thoughts The AI-curious: Those who have heard the buzz about AI and want to see what all the fuss is about.,Frustrated AI beginners: Those who have tried AI tools but haven't gotten the results they hoped for.,AI enthusiasts looking to level up: You're already using AI but want to sharpen your skills and dive deeper.,Small business owners: You're eager to harness AI's potential to boost your business but don't know where to start.,Professionals seeking an edge: You want to stay ahead of the curve in your industry by understanding AI applications.,Tech-savvy parents: You want to understand the AI your kids are using and help guide them in this new digital landscape.,Lifelong learners: You're fascinated by new technologies and want to understand one of the most impactful innovations of our time. 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Free Download Building Secure and Trustworthy LLMs Using NVIDIA Guardrails Released 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Skill Level: Intermediate | Genre: eLearning | Language: English + srt | Duration: 56m | Size: 106 MB Guardrails are essential components of large language models (LLMs) that can help to safeguard against misuse, define conversational standards, and enhance public trust in AI technologies. In this course, instructor Nayan Saxena explores the importance of ethical AI deployment to understand how NVIDIA NeMo Guardrails enforces LLM safety and integrity. Learn how to construct conversational guidelines using Colang, leverage advanced functionalities to craft dynamic LLM interactions, augment LLM capabilities with custom actions, and elevate response quality and contextual accuracy with retrieval-augmented generation (RAG). By witnessing guardrails in action and analyzing real-world case studies, you'll also acquire skills and best practices for implementing secure, user-centric AI systems. This course is ideal for AI practitioners, developers, and ethical technology advocates seeking to advance their knowledge in LLM safety, ethics, and application design for responsible AI. Homepage https://www.linkedin.com/learning/building-secure-and-trustworthy-llms-using-nvidia-guardrails TakeFile https://takefile.link/rmfoxbbqyz3i/mlqwd.Building.Secure.and.Trustworthy.LLMs.Using.NVIDIA.Guardrails.rar.html Rapidgator https://rg.to/file/0da052b9a0a4160d988f4c4d64d1cc68/mlqwd.Building.Secure.and.Trustworthy.LLMs.Using.NVIDIA.Guardrails.rar.html Fikper Free Download https://fikper.com/CxBV8UMA8L/mlqwd.Building.Secure.and.Trustworthy.LLMs.Using.NVIDIA.Guardrails.rar.html No Password - Links are Interchangeable

Free Download From Traditional ML to LLMs Published 9/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 39m | Size: 213 MB Bridging the gap from ML basics to advanced LLMs What you'll learn Leveraging traditional ML knowledge for working with LLMs Hands-on experience with PyTorch for LLMs Deeply understand the details of Transformer architecture Unfold the use-cases of LLMs for different tasks Discover new evaluation metrics specifically for LLMs Perform Text Classification and Text Summarization in Python Get familiar with concepts like RLHF or OpenAI API Confidence to make the first steps in LLMs Requirements Knowledge of traditional ML basic concepts and Python Description Unlock the most recent 'now' of machine learning with this hands-on, fast-paced crash course entitled "From Traditional ML to LLMs."Your Story:[Hypothetical] Anna, a seasoned ML engineer, had mastered traditional machine learning models, but every job listing screamed "LLMs." The world was moving on, and she needed to keep up. Learning Large Language Models sounded like a daunting leap-until she found a way to bridge her existing skills with the cutting-edge techniques she needed. This course was her solution.[Hypothetical] Jamal was a data scientist with strong ML experience, but transformers and tokenization seemed like a different universe. He needed to add LLMs to his skill set to stay competitive, and he didn't want theory; he wanted practical, hands-on applications that would help him shine in real-world projects.My Story: I've been where you are-armed with traditional ML knowledge but looking to level up. I struggled with endless tutorials and theories, but through persistence, I got hands-on and found the perfect way to apply my traditional ML expertise to LLMs. I went from logistic regression models to transformer-based LLMs, and now I want to help you do the same. By the end of this course, you'll confidently build and fine-tune LLMs using your existing knowledge, apply PyTorch, and solve real-world text-based challenges.What You'll Learn: In this course, I won't just throw theory at you. You'll gain real, actionable skills to bridge the gap from traditional ML to LLMs, helping you tackle practical challenges in the industry. Here's what you'll get:Core skills refreshed and connected to LLMs.A deep understanding of the famous Transformers.Practical insights into LLM concepts - from tokenization to RLHF.A hands-on project-based approach where you'll build a text classification and a summarization model using PyTorch.How This Course is Structured: I know learning LLMs can feel like stepping into a foreign world. So, I've designed this course to be practical and fun-no abstract concepts, just real-world applications. I'll walk you through exercises and examples based on actual ML-to-LLM workflows. Expect quizzes and assignments that you can apply directly to your work.FAQs:Do I need to know LLMs already? - Nope! We'll cover everything you need from basic architecture concepts to advanced LLMs.Will this course work for PyTorch beginners? - Absolutely! We guide you through the necessary steps to build and fine-tune your first models.Ready to close the gap between traditional ML and the next wave of AI innovation? Jump in and let's get started! Who this course is for Data scientists with good background in traditional ML but lacking any knowledge in LLMs Homepage https://www.udemy.com/course/from-traditional-ml-to-llms/ Rapidgator https://rg.to/file/454ab1e06a3d98fa2b41282d11e1439d/ggljj.From.Traditional.ML.to.LLMs.rar.html Fikper Free Download https://fikper.com/oFfXneabnk/ggljj.From.Traditional.ML.to.LLMs.rar.html No Password - Links are Interchangeable

pdf | 48.88 MB | English | Isbn:9781098160883 | Author: Kerrie Holley, Manish Mathur | Year: 2024 About ebook: LLMs and Generative AI for Healthcare: The Next Frontier https://rapidgator.net/file/a3f072f0dc073f65da94859492b0265e/ https://nitroflare.com/view/1502804AE3F2AE2/