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Download the fantastic book titled PyTorch Pocket Reference written by Joe Papa, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "PyTorch Pocket Reference", which was released on 11 May 2021. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.

Summary of PyTorch Pocket Reference by Joe Papa PDF

This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers. Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development-from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices. Learn basic PyTorch syntax and design patterns Create custom models and data transforms Train and deploy models using a GPU and TPU Train and test a deep learning classifier Accelerate training using optimization and distributed training Access useful PyTorch libraries and the PyTorch ecosystem

PyTorch Pocket Reference

Detail About PyTorch Pocket Reference PDF

  • Author : Joe Papa
  • Publisher : "O'Reilly Media, Inc."
  • Genre : Computers
  • Total Pages : 310 pages
  • ISBN : 1492089974
  • PDF File Size : 12,8 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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PyTorch Pocket Reference

PyTorch Pocket Reference
  • Publisher : "O'Reilly Media, Inc."
  • File Size : 52,7 Mb
  • Release Date : 11 May 2021
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This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns,

PyTorch Pocket Reference

PyTorch Pocket Reference
  • Publisher : O'Reilly Media
  • File Size : 49,5 Mb
  • Release Date : 14 September 2021
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This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns,

PyTorch Pocket Reference

PyTorch Pocket Reference
  • Publisher : "O'Reilly Media, Inc."
  • File Size : 31,6 Mb
  • Release Date : 11 May 2021
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This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns,

Machine Learning Pocket Reference

Machine Learning Pocket Reference
  • Publisher : "O'Reilly Media, Inc."
  • File Size : 41,7 Mb
  • Release Date : 27 August 2019
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With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use

Programming PyTorch for Deep Learning

Programming PyTorch for Deep Learning
  • Publisher : O'Reilly Media
  • File Size : 22,7 Mb
  • Release Date : 20 September 2019
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Deep learning is changing everything. This machine-learning method has already surpassed traditional computer vision techniques, and the same is happening with NLP. If you're looking to bring deep learning into

Deep Learning with PyTorch

Deep Learning with PyTorch
  • Publisher : Simon and Schuster
  • File Size : 52,7 Mb
  • Release Date : 01 July 2020
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“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of

Deep Learning with PyTorch Quick Start Guide

Deep Learning with PyTorch Quick Start Guide
  • Publisher : Packt Publishing Ltd
  • File Size : 42,9 Mb
  • Release Date : 24 December 2018
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Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.

Mastering PyTorch

Mastering PyTorch
  • Publisher : Packt Publishing Ltd
  • File Size : 43,7 Mb
  • Release Date : 12 February 2021
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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

Deep Learning with PyTorch

Deep Learning with PyTorch
  • Publisher : Packt Publishing Ltd
  • File Size : 24,7 Mb
  • Release Date : 23 February 2018
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Build neural network models in text, vision and advanced analytics using PyTorch Key Features Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and

GNU Emacs Pocket Reference

GNU Emacs Pocket Reference
  • Publisher : "O'Reilly Media, Inc."
  • File Size : 54,9 Mb
  • Release Date : 20 May 1999
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GNU Emacs is the most popular and widespread of the Emacs family of editors. It is also the most powerful and flexible. Unlike all other text editors, GNU Emacs is