Bøger af Matthew Rosch

Starting a PyTorch Developer and Deep Learning Engineer career? Check out this 'PyTorch Cookbook,' a comprehensive guide with essential recipes and solutions for PyTorch and the ecosystem. The book covers PyTorch deep learning development from beginner to expert in well-written chapters.The book simplifies neural networks, training, optimization, and deployment strategies chapter by chapter. The first part covers PyTorch basics, data preprocessing, tokenization, and vocabulary. Next, it builds CNN, RNN, Attentional Layers, and Graph Neural Networks. The book emphasizes distributed training, scalability, and multi-GPU training for real-world scenarios. Practical embedded systems, mobile development, and model compression solutions illuminate on-device AI applications. However, the book goes beyond code and algorithms. It also offers hands-on troubleshooting and debugging for end-to-end deep learning development. 'PyTorch Cookbook' covers data collection to deployment errors and provides detailed solutions to overcome them.This book integrates PyTorch with ONNX Runtime, PySyft, Pyro, Deep Graph Library (DGL), Fastai, and Ignite, showing you how to use them for your projects. This book covers real-time inferencing, cluster training, model serving, and cross-platform compatibility. You'll learn to code deep learning architectures, work with neural networks, and manage deep learning development stages. 'PyTorch Cookbook' is a complete manual that will help you become a confident PyTorch developer and a smart Deep Learning engineer. Its clear examples and practical advice make it a must-read for anyone looking to use PyTorch and advance in deep learning.Key LearningsComprehensive introduction to PyTorch, equipping readers with foundational skills for deep learning.Practical demonstrations of various neural networks, enhancing understanding through hands-on practice.Exploration of Graph Neural Networks (GNN), opening doors to cutting-edge research fields.In-depth insight into PyTorch tools and libraries, expanding capabilities beyond core functions.Step-by-step guidance on distributed training, enabling scalable deep learning and AI projects.Real-world application insights, bridging the gap between theoretical knowledge and practical execution.Focus on mobile and embedded development with PyTorch, leading to on-device AI.Emphasis on error handling and troubleshooting, preparing readers for real-world challenges.Advanced topics like real-time inferencing and model compression, providing future ready skill.Table of ContentIntroduction to PyTorch 2.0Deep Learning Building BlocksConvolutional Neural NetworksRecurrent Neural NetworksNatural Language ProcessingGraph Neural Networks (GNNs)Working with Popular PyTorch ToolsDistributed Training and ScalabilityMobile and Embedded Development

This book is a comprehensive guide to understanding and utilizing PyTorch 2.0 for deep learning applications. It starts with an introduction to PyTorch, its various advantages over other deep learning frameworks, and its blend with CUDA for GPU acceleration. We delve into the heart of PyTorch - tensors, learning their different types, properties, and operations. Through step-by-step examples, the reader learns to perform basic arithmetic operations on tensors, manipulate them, and understand errors related to tensor shapes.A substantial portion of the book is dedicated to illustrating how to build simple PyTorch models. This includes uploading and preparing datasets, defining the architecture, training, and predicting. It provides hands-on exercises with a real-world dataset. The book then dives into exploring PyTorch's nn module and gives a detailed comparison of different types of networks like Feedforward, RNN, GRU, CNN, and their combination.Further, the book delves into understanding the training process and PyTorch's optim module. It explores the overview of optimization algorithms like Gradient Descent, SGD, Mini-batch Gradient Descent, Momentum, Adagrad, and Adam. A separate chapter focuses on advanced concepts in PyTorch 2.0, like model serialization, optimization, distributed training, and PyTorch Quantization API.In the final chapters, the book discusses the differences between TensorFlow 2.0 and PyTorch 2.0 and the step-by-step process of migrating a TensorFlow model to PyTorch 2.0 using ONNX. It provides an overview of common issues encountered during this process and how to resolve them.Key LearningsA comprehensive introduction to PyTorch and CUDA for deep learning.Detailed understanding and operations on PyTorch tensors.Step-by-step guide to building simple PyTorch models.Insight into PyTorch's nn module and comparison of various network types.Overview of the training process and exploration of PyTorch's optim module.Understanding advanced concepts in PyTorch like model serialization and optimization.Knowledge of distributed training in PyTorch.Practical guide to using PyTorch's Quantization API.Differences between TensorFlow 2.0 and PyTorch 2.0.Guidance on migrating TensorFlow models to PyTorch using ONNX.Table of ContentIntroduction to Pytorch 2.0 and CUDA 11.8Getting Started with TensorsAdvanced Tensors OperationsBuilding Neural Networks with PyTorch 2.0Training Neural Networks in PyTorch 2.0PyTorch 2.0 AdvancedMigrating from TensorFlow to PyTorch 2.0End-to-End PyTorch Regression ModelAudienceA perfect and skillful book for every machine learning engineer, data scientist, AI engineer and data researcher who are passionately looking towards drawing actionable intelligence using PyTorch 2.0. Knowing Python and the basics of deep learning is all you need to sail through this book.