PyTorch Cookbook

<p><strong>Starting a PyTorch Developer and Deep Learning Engineer career?</strong> 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.</p><p><br></p><p><strong>The book simplifies neural networks training optimization and deployment strategies chapter by chapter.</strong> The first part covers PyTorch basics data preprocessing tokenization and vocabulary. Next it builds CNN RNN Attentional Layers and Graph Neural Networks. <strong>The book emphasizes distributed training scalability and multi-GPU training for real-world scenarios.</strong> Practical embedded systems mobile development and model compression solutions illuminate on-device AI applications. However<strong> 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.</strong></p><p><br></p><p><strong>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.</strong> 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.<strong> Its clear examples and practical advice make it a must-read for anyone looking to use PyTorch and advance in deep learning.</strong></p><h4><br></h4><h4>Key Learnings</h4><ul><li>Comprehensive introduction to PyTorch equipping readers with foundational skills for deep learning.</li><li>Practical demonstrations of various neural networks enhancing understanding through hands-on practice.</li><li>Exploration of Graph Neural Networks (GNN) opening doors to cutting-edge research fields.</li><li>In-depth insight into PyTorch tools and libraries expanding capabilities beyond core functions.</li><li>Step-by-step guidance on distributed training enabling scalable deep learning and AI projects.</li><li>Real-world application insights bridging the gap between theoretical knowledge and practical execution.</li><li>Focus on mobile and embedded development with PyTorch leading to on-device AI.</li><li>Emphasis on error handling and troubleshooting preparing readers for real-world challenges.</li><li>Advanced topics like real-time inferencing and model compression providing future ready skill.</li></ul><p><br></p><h4>Table of Content</h4><ol><li>Introduction to PyTorch 2.0</li><li>Deep Learning Building Blocks</li><li>Convolutional Neural Networks</li><li>Recurrent Neural Networks</li><li>Natural Language Processing</li><li>Graph Neural Networks (GNNs)</li><li>Working with Popular PyTorch Tools</li><li>Distributed Training and Scalability</li><li>Mobile and Embedded Development</li></ol><p><br></p>