Building Llms With Pytorch And Tensorflow
DOWNLOAD
Download Building Llms With Pytorch And Tensorflow PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Building Llms With Pytorch And Tensorflow book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Building Llms With Pytorch And Tensorflow
DOWNLOAD
Author : RICHARD D. CONTRERAS
language : en
Publisher:
Release Date : 2025
Building Llms With Pytorch And Tensorflow written by RICHARD D. CONTRERAS and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025 with categories.
Building Llms With Pytorch
DOWNLOAD
Author : Anand Trivedi
language : en
Publisher: BPB Publications
Release Date : 2025-03-13
Building Llms With Pytorch written by Anand Trivedi and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Computers categories.
DESCRIPTION PyTorch has become the go-to framework for building cutting-edge large language models (LLMs), enabling developers to harness the power of deep learning for natural language processing. This book serves as your practical guide to navigating the intricacies of PyTorch, empowering you to create your own LLMs from the ground up. You will begin by mastering PyTorch fundamentals, including tensors, autograd, and model creation, before diving into core neural network concepts like gradients, loss functions, and backpropagation. Progressing through regression and image classification with convolutional neural networks, you will then explore advanced image processing through object detection and segmentation. The book seamlessly transitions into NLP, covering RNNs, LSTMs, and attention mechanisms, culminating in the construction of Transformer-based LLMs, including a practical mini-GPT project. You will also get a strong understanding of generative models like VAEs and GANs. By the end of this book, you will possess the technical proficiency to build, train, and deploy sophisticated LLMs using PyTorch, equipping you to contribute to the rapidly evolving landscape of AI. WHAT YOU WILL LEARN ● Build and train PyTorch models for linear and logistic regression. ● Configure PyTorch environments and utilize GPU acceleration with CUDA. ● Construct CNNs for image classification and apply transfer learning techniques. ● Master PyTorch tensors, autograd, and build fundamental neural networks. ● Utilize SSD and YOLO for object detection and perform image segmentation. ● Develop RNNs and LSTMs for sequence modeling and text generation. ● Implement attention mechanisms and build Transformer-based language models. ● Create generative models using VAEs and GANs for diverse applications. ● Build and deploy your own mini-GPT language model, applying the acquired skills. WHO THIS BOOK IS FOR Software engineers, AI researchers, architects seeking AI insights, and professionals in finance, medical, engineering, and mathematics will find this book a comprehensive starting point, regardless of prior deep learning expertise. TABLE OF CONTENTS 1. Introduction to Deep Learning 2. Nuts and Bolts of AI with PyTorch 3. Introduction to Convolution Neural Network 4. Model Building with Custom Layers and PyTorch 2.0 5. Advances in Computer Vision: Transfer Learning and Object Detection 6. Advanced Object Detection and Segmentation 7. Mastering Object Detection with Detectron2 8. Introduction to RNNs and LSTMs 9. Understanding Text Processing and Generation in Machine Learning 10. Transformers Unleashed 11. Introduction to GANs: Building Blocks of Generative Models 12. Conditional GANs, Latent Spaces, and Diffusion Models 13. PyTorch 2.0: New Features, Efficient CUDA Usage, and Accelerated Model Training 14. Building Large Language Models from Scratch
Building Large Language Model Llm Applications
DOWNLOAD
Author : Anand Vemula
language : en
Publisher: Anand Vemula
Release Date :
Building Large Language Model Llm Applications written by Anand Vemula and has been published by Anand Vemula this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
"Building LLM Apps" is a comprehensive guide that equips readers with the knowledge and practical skills needed to develop applications utilizing large language models (LLMs). The book covers various aspects of LLM application development, starting from understanding the fundamentals of LLMs to deploying scalable and efficient solutions. Beginning with an introduction to LLMs and their importance in modern applications, the book explores the history, key concepts, and popular architectures like GPT and BERT. Readers learn how to set up their development environment, including hardware and software requirements, installing necessary tools and libraries, and leveraging cloud services for efficient development and deployment. Data preparation is essential for training LLMs, and the book provides insights into gathering and cleaning data, annotating and labeling data, and handling imbalanced data to ensure high-quality training datasets. Training large language models involves understanding training basics, best practices, distributed training techniques, and fine-tuning pre-trained models for specific tasks. Developing LLM applications requires designing user interfaces, integrating LLMs into existing systems, and building interactive features such as chatbots, text generation, sentiment analysis, named entity recognition, and machine translation. Advanced LLM techniques like prompt engineering, transfer learning, multi-task learning, and zero-shot learning are explored to enhance model capabilities. Deployment and scalability strategies are discussed to ensure smooth deployment of LLM applications while managing costs effectively. Security and ethics in LLM apps are addressed, covering bias detection, fairness, privacy, security, and ethical considerations to build responsible AI solutions. Real-world case studies illustrate the practical applications of LLMs in various domains, including customer service, healthcare, and finance. Troubleshooting and optimization techniques help readers address common issues and optimize model performance. Looking towards the future, the book highlights emerging trends and developments in LLM technology, emphasizing the importance of staying updated with advancements and adhering to ethical AI practices. "Building LLM Apps" serves as a comprehensive resource for developers, data scientists, and business professionals seeking to harness the power of large language models in their applications.
Deep Learning With Pytorch
DOWNLOAD
Author : Luca Pietro Giovanni Antiga
language : en
Publisher: Simon and Schuster
Release Date : 2020-07-01
Deep Learning With Pytorch written by Luca Pietro Giovanni Antiga and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-01 with Computers categories.
“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production
Building Llms For Production
DOWNLOAD
Author : Louis-François Bouchard
language : en
Publisher: Towards AI, Inc.
Release Date : 2024-05-21
Building Llms For Production written by Louis-François Bouchard and has been published by Towards AI, Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-21 with Computers categories.
“This is the most comprehensive textbook to date on building LLM applications - all essential topics in an AI Engineer's toolkit." - Jerry Liu, Co-founder and CEO of LlamaIndex (THE BOOK WAS UPDATED ON OCTOBER 2024) With amazing feedback from industry leaders, this book is an end-to-end resource for anyone looking to enhance their skills or dive into the world of AI and develop their understanding of Generative AI and Large Language Models (LLMs). It explores various methods to adapt "foundational" LLMs to specific use cases with enhanced accuracy, reliability, and scalability. Written by over 10 people on our Team at Towards AI and curated by experts from Activeloop, LlamaIndex, Mila, and more, it is a roadmap to the tech stack of the future. The book aims to guide developers through creating LLM products ready for production, leveraging the potential of AI across various industries. It is tailored for readers with an intermediate knowledge of Python. What's Inside this 470-page Book (Updated October 2024)? - Hands-on Guide on LLMs, Prompting, Retrieval Augmented Generation (RAG) & Fine-tuning - Roadmap for Building Production-Ready Applications using LLMs - Fundamentals of LLM Theory - Simple-to-Advanced LLM Techniques & Frameworks - Code Projects with Real-World Applications - Colab Notebooks that you can run right away Community access and our own AI Tutor Table of Contents - Chapter I Introduction to Large Language Models - Chapter II LLM Architectures & Landscape - Chapter III LLMs in Practice - Chapter IV Introduction to Prompting - Chapter V Retrieval-Augmented Generation - Chapter VI Introduction to LangChain & LlamaIndex - Chapter VII Prompting with LangChain - Chapter VIII Indexes, Retrievers, and Data Preparation - Chapter IX Advanced RAG - Chapter X Agents - Chapter XI Fine-Tuning - Chapter XII Deployment and Optimization Whether you're looking to enhance your skills or dive into the world of AI for the first time as a programmer or software student, our book is for you. From the basics of LLMs to mastering fine-tuning and RAG for scalable, reliable AI applications, we guide you every step of the way.
Deep Learning For Coders With Fastai And Pytorch
DOWNLOAD
Author : Jeremy Howard
language : en
Publisher: O'Reilly Media
Release Date : 2020-06-29
Deep Learning For Coders With Fastai And Pytorch written by Jeremy Howard and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-29 with Computers categories.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
Machine Learning With Pytorch And Scikit Learn
DOWNLOAD
Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-02-25
Machine Learning With Pytorch And Scikit Learn written by Sebastian Raschka and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-25 with Computers categories.
This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.
Building Ai Agents With Llms Rag And Knowledge Graphs
DOWNLOAD
Author : Salvatore Raieli
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-07-11
Building Ai Agents With Llms Rag And Knowledge Graphs written by Salvatore Raieli and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-11 with Computers categories.
Master LLM fundamentals to advanced techniques like RAG, reinforcement learning, and knowledge graphs to build, deploy, and scale intelligent AI agents that reason, retrieve, and act autonomously Key Features Implement RAG and knowledge graphs for advanced problem-solving Leverage innovative approaches like LangChain to create real-world intelligent systems Integrate large language models, graph databases, and tool use for next-gen AI solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis AI agents book addresses the challenge of building AI that not only generates text but also grounds its responses in real data and takes action. Authored by AI specialists with deep expertise in drug discovery and systems optimization, this guide empowers you to leverage retrieval-augmented generation (RAG), knowledge graphs, and agent-based architectures to engineer truly intelligent behavior. By combining large language models (LLMs) with up-to-date information retrieval and structured knowledge, you'll create AI agents capable of deeper reasoning and more reliable problem-solving. Inside, you'll find a practical roadmap from concept to implementation. You’ll discover how to connect language models with external data via RAG pipelines for increasing factual accuracy and incorporate knowledge graphs for context-rich reasoning. The chapters will help you build and orchestrate autonomous agents that combine planning, tool use, and knowledge retrieval to achieve complex goals. Concrete Python examples built on popular libraries, along with real-world case studies, reinforce each concept and show you how these techniques come together. By the end of this book, you’ll be well-equipped to build intelligent AI agents that reason, retrieve, and interact dynamically, empowering you to deploy powerful AI solutions across industries.What you will learn Learn how LLMs work, their structure, uses, and limits, and design RAG pipelines to link them to external data Build and query knowledge graphs for structured context and factual grounding Develop AI agents that plan, reason, and use tools to complete tasks Integrate LLMs with external APIs and databases to incorporate live data Apply techniques to minimize hallucinations and ensure accurate outputs Orchestrate multiple agents to solve complex, multi-step problems Optimize prompts, memory, and context handling for long-running tasks Deploy and monitor AI agents in production environments Who this book is for If you are a data scientist or researcher who wants to learn how to create and deploy an AI agent to solve limitless tasks, this book is for you. To get the most out of this book, you should have basic knowledge of Python and Gen AI. This book is also excellent for experienced data scientists who want to explore state-of-the-art developments in LLM and LLM-based applications.
Building And Fine Tuning Llms From Scratch
DOWNLOAD
Author : StoryBuddiesPlay
language : en
Publisher: StoryBuddiesPlay
Release Date : 2024-09-10
Building And Fine Tuning Llms From Scratch written by StoryBuddiesPlay and has been published by StoryBuddiesPlay this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-10 with Computers categories.
"Building and Fine-Tuning LLMs from Scratch" is an essential guide for AI practitioners, researchers, and enthusiasts looking to master the art of creating and optimizing large language models. This comprehensive resource covers everything from fundamental concepts to cutting-edge techniques, providing readers with the knowledge and skills needed to develop state-of-the-art language AI systems. With practical examples, in-depth explanations, and expert insights, this book is your roadmap to becoming proficient in LLM architecture, training, fine-tuning, and deployment. Whether you're a seasoned professional or an ambitious newcomer, this guide will empower you to push the boundaries of what's possible in natural language processing and AI. Large Language Models, AI development, Natural Language Processing, Machine Learning, Deep Learning, Transformer Architecture, Fine-tuning techniques, Neural Networks, Text Generation, Language AI
Deep Learning And The Game Of Go
DOWNLOAD
Author : Kevin Ferguson
language : en
Publisher: Simon and Schuster
Release Date : 2019-01-06
Deep Learning And The Game Of Go written by Kevin Ferguson and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-06 with Computers categories.
Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning