PyTorch Recipes: A Problem-Solution Approach (PyTorch 食譜：問題解決方法)

Pradeepta Mishra

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商品描述

Get up to speed with the deep learning concepts of Pytorch using a problem-solution approach. Starting with an introduction to PyTorch, you'll get familiarized with tensors, a type of data structure used to calculate arithmetic operations and also learn how they operate. You will then take a look at probability distributions using PyTorch and get acquainted with its concepts. Further you will dive into transformations and graph computations with PyTorch. Along the way you will take a look at common issues faced with neural network implementation and tensor differentiation, and get the best solutions for them. 
 
Moving on to algorithms; you will learn how PyTorch works with supervised and unsupervised algorithms. You will see how convolutional neural networks, deep neural networks, and recurrent neural networks work using PyTorch. In conclusion you will get acquainted with natural language processing and text processing using PyTorch.
 
What You Will Learn
  •  
  • Master tensor operations for dynamic graph-based calculations using PyTorch
  • Create PyTorch transformations and graph computations for neural networks
  • Carry out supervised and unsupervised learning using PyTorch 
  • Work with deep learning algorithms such as CNN and RNN
  • Build LSTM models in PyTorch 
  • Use PyTorch for text processing 
Who This Book Is For
 
Readers wanting to dive straight into programming PyTorch.
 

商品描述(中文翻譯)

深入了解使用 PyTorch 的深度學習概念，並以問題解決的方式進行學習。從 PyTorch 的介紹開始，您將熟悉張量（tensors），這是一種用於計算算術運算的數據結構，並學習它們的操作方式。接著，您將使用 PyTorch 探索概率分佈並熟悉其概念。此外，您還將深入研究 PyTorch 中的轉換和圖計算。在此過程中，您將瞭解神經網絡實現和張量微分所面臨的常見問題，並獲得最佳解決方案。

接下來，您將學習 PyTorch 如何與監督和非監督算法一起工作。您將使用 PyTorch 瞭解卷積神經網絡、深度神經網絡和循環神經網絡的工作原理。最後，您將熟悉使用 PyTorch 進行自然語言處理和文本處理。

本書的學習重點包括：
- 掌握使用 PyTorch 進行基於動態圖的張量操作
- 創建用於神經網絡的 PyTorch 轉換和圖計算
- 使用 PyTorch 進行監督和非監督學習
- 使用 CNN 和 RNN 等深度學習算法
- 在 PyTorch 中構建 LSTM 模型
- 使用 PyTorch 進行文本處理

本書適合以下讀者：
- 希望直接進入 PyTorch 編程的讀者。

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