Mastering Pytorch Second Edition
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Mastering Pytorch
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Author : Ashish Ranjan Jha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-31
Mastering Pytorch written by Ashish Ranjan Jha 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 2024-05-31 with Computers categories.
Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Understand how to use PyTorch to build advanced neural network models Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks Book DescriptionPyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation, using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text, vision, and music generation models using PyTorch Build a deep Q-network (DQN) model in PyTorch Deploy PyTorch models on mobile devices (Android and iOS) Become well versed in rapid prototyping using PyTorch with fastai Perform neural architecture search effectively using AutoML Easily interpret machine learning models using Captum Design ResNets, LSTMs, and graph neural networks (GNNs) Create language and vision transformer models using Hugging Face Who this book is for This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.
Mastering Pytorch Second Edition
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Author : Ashish Ranjan Jha
language : en
Publisher:
Release Date : 2024-05-31
Mastering Pytorch Second Edition written by Ashish Ranjan Jha and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-31 with Computers categories.
Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks Purchase of the print or Kindle book includes a free eBook in PDF format Key Features: - Understand how to use PyTorch to build advanced neural network models - Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker - Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks Book Description: PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most from your data and build complex neural network models. You'll build convolutional neural networks for image classification and recurrent neural networks and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You'll deploy PyTorch models to production, including mobile devices. Finally, you'll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai for prototyping models to training models using PyTorch Lightning. You'll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models. What You Will Learn: - Implement text, vision, and music generating models using PyTorch - Build a deep Q-network (DQN) model in PyTorch - Deploy PyTorch models on mobile devices (Android and iOS) - Become well-versed with rapid prototyping using PyTorch with fast.ai - Perform neural architecture search effectively using AutoML - Easily interpret machine learning models using Captum - Design ResNets, LSTMs, and graph neural networks (GNNs) - Create language and vision transformer models using Hugging Face Who this book is for: This deep learning with PyTorch book is for data scientists, machine learning engineers, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning models using PyTorch. This book is ideal for those looking to switch from TensorFlow to PyTorch. Working knowledge of deep learning with Python is required.
Mastering Pytorch
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Author : Ashish Ranjan Jha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-02-12
Mastering Pytorch written by Ashish Ranjan Jha 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 2021-02-12 with Computers categories.
Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more Book DescriptionDeep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learn Implement text and music generating models using PyTorch Build a deep Q-network (DQN) model in PyTorch Export universal PyTorch models using Open Neural Network Exchange (ONNX) Become well-versed with rapid prototyping using PyTorch with fast.ai Perform neural architecture search effectively using AutoML Easily interpret machine learning (ML) models written in PyTorch using Captum Design ResNets, LSTMs, Transformers, and more using PyTorch Find out how to use PyTorch for distributed training using the torch.distributed API Who this book is for This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.
Deep Learning With Pytorch Second Edition
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Author : Luca Antiga
language : en
Publisher: Manning
Release Date : 2025-11-25
Deep Learning With Pytorch Second Edition written by Luca Antiga and has been published by Manning this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-25 with Computers categories.
Everything you need to create neural networks with PyTorch, including Large Language and diffusion models. Deep Learning with PyTorch, Second Edition updates the bestselling original guide with new insights into the transformers architecture and generative AI models. Instantly familiar to anyone who knows PyData tools like NumPy and scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. In Deep Learning with PyTorch, Second Edition you’ll find: • Deep learning fundamentals reinforced with hands-on projects • Mastering PyTorch's flexible APIs for neural network development • Implementing CNNs, RNNs and Transformers • Optimizing models for training and deployment • Generative AI models to create images and text In Deep Learning with PyTorch, Second Edition you’ll learn how to create your own neural network and deep learning systems and take full advantage of PyTorch’s built-in tools for automatic differentiation, hardware acceleration, distributed training, and more. PyTorch makes it easy to build the powerful neural networks that underpin many modern advances in artificial intelligence. This second edition has been thoroughly revised by PyTorch core developer Howard Huang to cover the latest features and applications, including generative AI models. About the book Deep Learning with PyTorch, Second Edition is a hands-on guide to modern machine learning with PyTorch. You’ll discover how easy PyTorch makes it to build your entire DL pipeline, including using the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results. Each new technique you learn is put into action to build a full-size medical image classifier chapter-by-chapter. In this modernized second edition, you’ll find new coverage of how to develop and train groundbreaking generative AI models. You’ll learn about the foundational building blocks of transformers to create large language models and generate exciting images by building your own diffusion model. Plus, you'll discover ways to improve your results by training with augmented data, make improvements to the model architecture, and perform fine tuning. About the reader For Python programmers with an interest in machine learning. About the author Howard Huang is a software engineer and developer on the PyTorch library. During his tenure at PyTorch he has focused on large scale, distributed training. Eli Stevens, Luca Antiga, and Thomas Viehmann authored the first edition of Deep Learning with PyTorch. Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
Accelerate Model Training With Pytorch 2 X
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Author : Maicon Melo Alves
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-04-30
Accelerate Model Training With Pytorch 2 X written by Maicon Melo Alves 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 2024-04-30 with Computers categories.
Dramatically accelerate the building process of complex models using PyTorch to extract the best performance from any computing environment Key Features Reduce the model-building time by applying optimization techniques and approaches Harness the computing power of multiple devices and machines to boost the training process Focus on model quality by quickly evaluating different model configurations Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThis book, written by an HPC expert with over 25 years of experience, guides you through enhancing model training performance using PyTorch. Here you’ll learn how model complexity impacts training time and discover performance tuning levels to expedite the process, as well as utilize PyTorch features, specialized libraries, and efficient data pipelines to optimize training on CPUs and accelerators. You’ll also reduce model complexity, adopt mixed precision, and harness the power of multicore systems and multi-GPU environments for distributed training. By the end, you'll be equipped with techniques and strategies to speed up training and focus on building stunning models.What you will learn Compile the model to train it faster Use specialized libraries to optimize the training on the CPU Build a data pipeline to boost GPU execution Simplify the model through pruning and compression techniques Adopt automatic mixed precision without penalizing the model's accuracy Distribute the training step across multiple machines and devices Who this book is for This book is for intermediate-level data scientists who want to learn how to leverage PyTorch to speed up the training process of their machine learning models by employing a set of optimization strategies and techniques. To make the most of this book, familiarity with basic concepts of machine learning, PyTorch, and Python is essential. However, there is no obligation to have a prior understanding of distributed computing, accelerators, or multicore processors.
Python Machine Learning By Example
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-07-31
Python Machine Learning By Example written by Yuxi (Hayden) Liu 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 2024-07-31 with Computers categories.
Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandas. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Discover new and updated content on NLP transformers, PyTorch, and computer vision modeling Includes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutions Implement ML models, such as neural networks and linear and logistic regression, from scratch Book DescriptionThe fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide. *Email sign-up and proof of purchase requiredWhat you will learn Follow machine learning best practices throughout data preparation and model development Build and improve image classifiers using convolutional neural networks (CNNs) and transfer learning Develop and fine-tune neural networks using TensorFlow and PyTorch Analyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIP Build classifiers using support vector machines (SVMs) and boost performance with PCA Avoid overfitting using regularization, feature selection, and more Who this book is for This expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.
Mastering Pytorch And Machine Learning
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Author : Ed Norex
language : en
Publisher: Independently Published
Release Date : 2024-02-19
Mastering Pytorch And Machine Learning written by Ed Norex and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-19 with Computers categories.
Mastering PyTorch and Machine Learning: Essential Techniques is a comprehensive guide designed to take you on a transformative journey through the world of PyTorch and machine learning. Whether you are a novice eager to dive into the realm of deep learning or an experienced developer aiming to refine your skills, this book serves as your indispensable companion. Dive deep into the fundamentals of PyTorch, exploring its dynamic ecosystem, and unravel the mysteries of tensors, autograd mechanics, and neural network architectures. With detailed discussions on the intricacies of training deep learning models, working with convolutional and recurrent neural networks, and the fascinating world of generative models, this book equips you with the knowledge to tackle modern machine learning challenges. Learn to implement reinforcement learning, manage performance and scalability, and master the art of deploying PyTorch models efficiently. Through practical examples and expert insights, Ed Norex provides the tools and techniques needed to harness the full potential of PyTorch, enabling you to create robust and scalable machine learning models. Mastering PyTorch and Machine Learning: Essential Techniques is more than just a book; it's a gateway to unlocking the future of intelligent applications. Embark on this journey to elevate your skills and become proficient in the art and science of machine learning with PyTorch.
Learning Pytorch 2 0 Second Edition
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Author : Matthew Rosch
language : en
Publisher: GitforGits
Release Date : 2024-10-05
Learning Pytorch 2 0 Second Edition written by Matthew Rosch and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-05 with Computers categories.
"Learning PyTorch 2.0, Second Edition" is a fast-learning, hands-on book that emphasizes practical PyTorch scripting and efficient model development using PyTorch 2.3 and CUDA 12. This edition is centered on practical applications and presents a concise methodology for attaining proficiency in the most recent features of PyTorch. The book presents a practical program based on the fish dataset which provides step-by-step guidance through the processes of building, training and deploying neural networks, with each example prepared for immediate implementation. Given your familiarity with machine learning and neural networks, this book offers concise explanations of foundational topics, allowing you to proceed directly to the practical, advanced aspects of PyTorch programming. The key learnings include the design of various types of neural networks, the use of torch.compile() for performance optimization, the deployment of models using TorchServe, and the implementation of quantization for efficient inference. Furthermore, you will also learn to migrate TensorFlow models to PyTorch using the ONNX format. The book employs essential libraries, including torchvision, torchserve, tf2onnx, onnxruntime, and requests, to facilitate seamless integration of PyTorch with production environments. Regardless of whether the objective is to fine-tune models or to deploy them on a large scale, this second edition is designed to ensure maximum efficiency and speed, with practical PyTorch scripting at the forefront of each chapter. Key Learnings Master tensor manipulations and advanced operations using PyTorch's efficient tensor libraries. Build feedforward, convolutional, and recurrent neural networks from scratch. Implement transformer models for modern natural language processing tasks. Use CUDA 12 and mixed precision training (AMP) to accelerate model training and inference. Deploy PyTorch models in production using TorchServe, including multi-model serving and versioning. Migrate TensorFlow models to PyTorch using ONNX format for seamless cross-framework compatibility. Optimize neural network architectures using torch.compile() for improved speed and efficiency. Utilize PyTorch's Quantization API to reduce model size and speed up inference. Setup custom layers and architectures for neural networks to tackle domain-specific problems. Monitor and log model performance in real-time using TorchServe's built-in tools and configurations. Table of Content Introduction To PyTorch 2.3 and CUDA 12 Getting Started with Tensors Building Neural Networks with PyTorch Training Neural Networks Advanced Neural Network Architectures Quantization and Model Optimization Migrating TensorFlow to PyTorch Deploying PyTorch Models with TorchServe
Python Deep Learning
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Author : Ivan Vasilev
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-01-16
Python Deep Learning written by Ivan Vasilev 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 2019-01-16 with Computers categories.
Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries Key Features Build a strong foundation in neural networks and deep learning with Python libraries Explore advanced deep learning techniques and their applications across computer vision and NLP Learn how a computer can navigate in complex environments with reinforcement learning Book DescriptionWith the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you’ll explore deep learning, and learn how to put machine learning to use in your projects. This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You’ll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You’ll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you’ll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota. By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.What you will learn Grasp the mathematical theory behind neural networks and deep learning processes Investigate and resolve computer vision challenges using convolutional networks and capsule networks Solve generative tasks using variational autoencoders and Generative Adversarial Networks Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models Explore reinforcement learning and understand how agents behave in a complex environment Get up to date with applications of deep learning in autonomous vehicles Who this book is for This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.
Mastering Pytorch 2 6
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Author : Frances F Thompson
language : en
Publisher: Independently Published
Release Date : 2025-03-13
Mastering Pytorch 2 6 written by Frances F Thompson and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Computers categories.
Harness the Power of PyTorch 2.6 to Build Cutting-Edge AI Models PyTorch has become the go-to framework for deep learning, and with version 2.6, it's more powerful and efficient than ever. Whether you're a data scientist, AI researcher, or developer, this book will guide you through mastering PyTorch's latest features to build, train, and deploy neural networks with confidence. What's Inside? This hands-on guide takes you from the fundamentals of PyTorch to advanced deep-learning techniques, covering everything from building neural networks to deploying scalable AI solutions. You'll learn how to leverage PyTorch 2.6's latest optimizations, including compile mode, improved ONNX export, and enhanced model efficiency. Through real-world projects and step-by-step tutorials, you'll gain the skills to develop state-of-the-art AI models for computer vision, natural language processing, and more. Why Choose This Book? ✅ Comprehensive Coverage - Covers the latest PyTorch 2.6 features, best practices, and deployment strategies. ✅ Hands-on Learning - Includes practical examples, real-world projects, and step-by-step code implementations. ✅ Performance Optimization - Learn how to optimize models for speed, efficiency, and scalability. ✅ Production-Ready AI - Master techniques for deploying AI models with ONNX, TorchServe, and cloud integration. ✅ Expert Insights - Authored by industry professionals with deep learning expertise. Get Started Today! Whether you're building your first neural network or fine-tuning advanced AI models, Mastering PyTorch 2.6 equips you with the skills to take your deep-learning projects to the next level. Grab your copy now and start building intelligent systems with PyTorch!