Deep Learning with PyTorch 1.x - Second Edition

Mitchell, Laura, K, Sri Yogesh, Subramanian, Vishnu

  • 出版商: Packt Publishing
  • 出版日期: 2019-11-29
  • 定價: $1,360
  • 售價: 8.0$1,088
  • 語言: 英文
  • 頁數: 304
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1838553002
  • ISBN-13: 9781838553005
  • 相關分類: DeepLearning
  • 立即出貨 (庫存=1)

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商品描述

Learn
  • Build text classification and language modeling systems using neural networks
  • Implement transfer learning using advanced CNN architectures
  • Use deep reinforcement learning techniques to solve optimization problems in PyTorch
  • Mix multiple models for a powerful ensemble model
  • Build image classifiers by implementing CNN architectures using PyTorch
  • Get up to speed with reinforcement learning, GANs, LSTMs, and RNNs with real-world examples
About

PyTorch is gaining the attention of deep learning researchers and data science professionals due to its accessibility and efficiency, along with the fact that it's more native to the Python way of development. This book will get you up and running with this cutting-edge deep learning library, effectively guiding you through implementing deep learning concepts.

In this second edition, you'll learn the fundamental aspects that power modern deep learning, and explore the new features of the PyTorch 1.x library. You'll understand how to solve real-world problems using CNNs, RNNs, and LSTMs, along with discovering state-of-the-art modern deep learning architectures, such as ResNet, DenseNet, and Inception. You'll then focus on applying neural networks to domains such as computer vision and NLP. Later chapters will demonstrate how to build, train, and scale a model with PyTorch and also cover complex neural networks such as GANs and autoencoders for producing text and images. In addition to this, you'll explore GPU computing and how it can be used to perform heavy computations. Finally, you'll learn how to work with deep learning-based architectures for transfer learning and reinforcement learning problems.

By the end of this book, you'll be able to confidently and easily implement deep learning applications in PyTorch.

Features
  • Gain a thorough understanding of the PyTorch framework and learn to implement neural network architectures
  • Understand GPU computing to perform heavy deep learning computations using Python
  • Apply cutting-edge natural language processing (NLP) techniques to solve problems with textual data

商品描述(中文翻譯)



學習


  • 使用神經網絡構建文本分類和語言建模系統

  • 使用高級CNN架構實現遷移學習

  • 使用PyTorch解決優化問題的深度強化學習技術

  • 通過混合多個模型構建強大的集成模型

  • 使用PyTorch實現CNN架構構建圖像分類器

  • 通過真實世界的例子了解強化學習、GAN、LSTM和RNN





關於

由於其易用性和高效性,以及更貼近Python開發方式,PyTorch正吸引著深度學習研究人員和數據科學專業人士的關注。本書將引導您深入了解這個尖端的深度學習庫,並有效地指導您實現深度學習概念。

在第二版中,您將學習支持現代深度學習的基本方面,並探索PyTorch 1.x庫的新功能。您將了解如何使用CNN、RNN和LSTM解決現實世界的問題,並探索ResNet、DenseNet和Inception等最先進的現代深度學習架構。然後,您將專注於將神經網絡應用於計算機視覺和自然語言處理等領域。後面的章節將演示如何使用PyTorch構建、訓練和擴展模型,並涵蓋用於生成文本和圖像的複雜神經網絡,如GAN和自編碼器。此外,您還將探索GPU計算以及如何使用它進行大量計算。最後,您將學習如何使用基於深度學習的架構解決遷移學習和強化學習問題。

通過閱讀本書,您將能夠自信且輕鬆地在PyTorch中實現深度學習應用。





特點


  • 深入了解PyTorch框架,並學習實現神經網絡架構

  • 了解使用Python進行GPU計算,執行大量深度學習計算

  • 應用尖端的自然語言處理(NLP)技術解決文本數據問題