Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch

Vishnu Subramanian

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商品描述

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 flexibility and learn how to implement them in various scenarios;
  • Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples;

Book Description

Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, Tensorflow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics.

This book will get you up and running with one of the most cutting-edge deep learning libraries―PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images.

By the end of the book, you'll be able to implement deep learning applications in PyTorch

商品描述(中文翻譯)

使用PyTorch建立文字、視覺和高級分析的神經網絡模型

主要特點:

- 學習使用PyTorch實現尖端的深度學習算法。
- 訓練神經網絡以提高速度和靈活性,並學習如何在各種場景中實現它們。
- 通過實際示例介紹各種高級神經網絡架構,如ResNet、Inception、DenseNet等。

書籍描述:

深度學習驅動著世界上最智能的系統,如Google Voice、Siri和Alexa。強大的硬件(如GPU)、軟件框架(如PyTorch、Keras、Tensorflow和CNTK)以及大數據的可用性使得在文本、視覺和高級分析領域解決問題變得更加容易。

本書將帶您快速上手使用最尖端的深度學習庫之一 - PyTorch。由於其易用性、高效性和更貼近Python開發方式,PyTorch吸引了深度學習研究人員和數據科學專業人士的關注。您將首先安裝PyTorch,然後迅速學習支持現代深度學習的各種基本模塊。您還將學習如何使用CNN、RNN、LSTM和其他網絡來解決現實世界的問題。本書解釋了各種最先進的深度學習架構的概念,如ResNet、DenseNet、Inception和Seq2Seq,但不深入探討其背後的數學原理。在本書的過程中,您還將了解GPU計算。您將看到如何使用PyTorch訓練模型,並深入研究生成文本和圖像的複雜神經網絡。

通過閱讀本書,您將能夠在PyTorch中實現深度學習應用。