Mastering PyTorch. (Record no. 14293)
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| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250616151211.0 |
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| fixed length control field | 250616s||||||||||||||||o||||||||||| |d |
| 024 80 - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 9781801079969 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | PACKT |
| Transcribing agency | PACKT |
| 041 ## - LANGUAGE CODE | |
| Language code of text/sound track or separate title | en |
| 044 ## - COUNTRY OF PUBLISHING/PRODUCING ENTITY CODE | |
| MARC country code | GB |
| 100 0# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Ashish Ranjan Jha |
| Relator term | author. |
| 245 00 - TITLE STATEMENT | |
| Title | Mastering PyTorch. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | GB: |
| Name of publisher, distributor, etc. | Packt, |
| Date of publication, distribution, etc. | 2024-05-31. |
| 263 ## - PROJECTED PUBLICATION DATE | |
| Projected publication date | 2024-05-31 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | GB: |
| Name of producer, publisher, distributor, manufacturer | Packt, |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 558. |
| 377 ## - ASSOCIATED LANGUAGE | |
| Language code | en |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | <p><b>Master advanced techniques and algorithms for machine learning with PyTorch using real-world examples<br/><br/>Updated for PyTorch 2.x, including integration with Hugging Face, mobile deployment, diffusion models, and graph neural networks<br/>Purchase of the print or Kindle book includes a free eBook in PDF format</b></p><h4>Key Features</h4><ul><li>Understand how to use PyTorch to build advanced neural network models</li><li>Get the best from PyTorch by working with Hugging Face, fastai, PyTorch Lightning, PyTorch Geometric, Flask, and Docker</li><li>Unlock faster training with multiple GPUs and optimize model deployment using efficient inference frameworks</li></ul><h4>Book Description</h4>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 out of your data and build complex neural network models.<br/>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.<br/>By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.<h4>What you will learn</h4><ul><li>Implement text, vision, and music generation models using PyTorch</li><li>Build a deep Q-network (DQN) model in PyTorch</li><li>Deploy PyTorch models on mobile devices (Android and iOS)</li><li>Become well versed in rapid prototyping using PyTorch with fastai</li><li>Perform neural architecture search effectively using AutoML</li><li>Easily interpret machine learning models using Captum</li><li>Design ResNets, LSTMs, and graph neural networks (GNNs)</li><li>Create language and vision transformer models using Hugging Face</li></ul><h4>Who this book is for</h4>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. |
| 538 ## - SYSTEM DETAILS NOTE | |
| System details note | Data in extended ASCII character set. |
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| System details note | Mode of access: Internet. |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | PACKT |
| 773 0# - HOST ITEM ENTRY | |
| Title | Mastering PyTorch |
| Place, publisher, and date of publication | GB,Packt,2024-05-31 |
| Physical description | 558 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://learning.packt.com/product/472450">https://learning.packt.com/product/472450</a> |
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