{"product_id":"learning-pytorch-2-0-second-edition-utilize-pytorch-2-3-and-cuda-12-to-experiment-neural-networks-and-deep-learning-models-paperback","title":"Learning PyTorch 2.0, Second Edition: Utilize PyTorch 2.3 and CUDA 12 to experiment neural networks and deep learning models - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eMatthew Rosch\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\"Learning PyTorch 2.0, Second Edition\" is a \u003cstrong\u003efast-learning, hands-on book that emphasizes practical PyTorch scripting and efficient model development using PyTorch 2.3 and CUDA 12\u003c\/strong\u003e. 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 \u003cstrong\u003estep-by-step guidance through the processes of building, training and deploying neural networks, with each example prepared for immediate implementation\u003c\/strong\u003e. 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. \u003c\/p\u003e\u003cp\u003eThe \u003cstrong\u003ekey 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\u003c\/strong\u003e. Furthermore, you will also \u003cstrong\u003elearn to migrate TensorFlow models to PyTorch using the ONNX format\u003c\/strong\u003e. The book employs \u003cstrong\u003eessential libraries, including torchvision, torchserve, tf2onnx, onnxruntime, and requests, to facilitate seamless integration of PyTorch with production environments\u003c\/strong\u003e.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003eKey Learnings\u003cp\u003eMaster tensor manipulations and advanced operations using PyTorch's efficient tensor libraries.\u003c\/p\u003e\u003cp\u003eBuild feedforward, convolutional, and recurrent neural networks from scratch.\u003c\/p\u003e\u003cp\u003eImplement transformer models for modern natural language processing tasks.\u003c\/p\u003e\u003cp\u003eUse CUDA 12 and mixed precision training (AMP) to accelerate model training and inference.\u003c\/p\u003e\u003cp\u003eDeploy PyTorch models in production using TorchServe, including multi-model serving and versioning.\u003c\/p\u003e\u003cp\u003eMigrate TensorFlow models to PyTorch using ONNX format for seamless cross-framework compatibility.\u003c\/p\u003e\u003cp\u003eOptimize neural network architectures using torch.compile() for improved speed and efficiency.\u003c\/p\u003e\u003cp\u003eUtilize PyTorch's Quantization API to reduce model size and speed up inference.\u003c\/p\u003e\u003cp\u003eSetup custom layers and architectures for neural networks to tackle domain-specific problems.\u003c\/p\u003e\u003cp\u003eMonitor and log model performance in real-time using TorchServe's built-in tools and configurations.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003eTable of Content\u003col\u003eIntroduction To PyTorch 2.3 and CUDA 12Getting Started with TensorsBuilding Neural Networks with PyTorchTraining Neural NetworksAdvanced Neural Network ArchitecturesQuantization and Model OptimizationMigrating TensorFlow to PyTorchDeploying PyTorch Models with TorchServe\u003c\/ol\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 192\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.41 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e October 05, 2024\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":48288978862329,"sku":"9788119177912","price":80.98,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/9pZ1PyySiq9788119177912.webp?v=1776258012","url":"https:\/\/bookscloud.io\/products\/learning-pytorch-2-0-second-edition-utilize-pytorch-2-3-and-cuda-12-to-experiment-neural-networks-and-deep-learning-models-paperback","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}