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

Pradeepta Mishra

PyTorch Recipes: A Problem-Solution Approach 預覽內頁
  • PyTorch Recipes: A Problem-Solution Approach-preview-1
  • PyTorch Recipes: A Problem-Solution Approach-preview-2
  • PyTorch Recipes: A Problem-Solution Approach-preview-3
  • PyTorch Recipes: A Problem-Solution Approach-preview-4
  • PyTorch Recipes: A Problem-Solution Approach-preview-5
  • PyTorch Recipes: A Problem-Solution Approach-preview-6
  • PyTorch Recipes: A Problem-Solution Approach-preview-7
  • PyTorch Recipes: A Problem-Solution Approach-preview-8
PyTorch Recipes: A Problem-Solution Approach-preview-1

買這商品的人也買了...

相關主題

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)




透過問題解決的方法，快速掌握 PyTorch 的深度學習概念。首先介紹 PyTorch，您將熟悉張量（tensor），這是一種用於計算算術運算的數據結構，並了解它們的運作方式。接著，您將使用 PyTorch 了解機率分佈的概念。然後，您將深入探討 PyTorch 的轉換和圖形計算。在此過程中，您將了解神經網絡實現和張量微分中常見的問題，並獲得最佳解決方案。

 

接下來是算法；您將學習 PyTorch 如何與監督式和非監督式算法一起工作。您將看到卷積神經網絡（CNN）、深度神經網絡（DNN）和遞迴神經網絡（RNN）如何使用 PyTorch 運作。最後，您將了解如何使用 PyTorch 進行自然語言處理和文本處理。

 

您將學到的內容



  •  

  • 掌握使用 PyTorch 進行動態圖形計算的張量運算

  • 為神經網絡創建 PyTorch 轉換和圖形計算

  • 使用 PyTorch 執行監督式和非監督式學習

  • 使用深度學習算法，如 CNN 和 RNN

  • 在 PyTorch 中構建 LSTM 模型

  • 使用 PyTorch 進行文本處理


本書適合誰閱讀

 

希望直接進入 PyTorch 編程的讀者。

 


類似商品