Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features

Jha, Ashish Ranjan

Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features

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商品描述

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 a wide range of tasks by implementing deep learning algorithms and techniques
  • Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more

Book Description

Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models.

The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai.

By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

What you will learn

  • Implement text and music generating models using PyTorch
  • Build a deep Q-network (DQN) model in PyTorch
  • Export universal PyTorch models using Open Neural Network Exchange (ONNX)
  • Become well-versed with rapid prototyping using PyTorch with fast.ai
  • Perform neural architecture search effectively using AutoML
  • Easily interpret machine learning (ML) models written in PyTorch using Captum
  • Design ResNets, LSTMs, Transformers, and more using PyTorch
  • Find out how to use PyTorch for distributed training using the torch.distributed API

Who this book is for

This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.

商品描述(中文翻譯)

精通 PyTorch 的深度學習高級技術和算法，並使用實際案例進行學習

主要特點


  • 了解如何使用 PyTorch 1.x 構建高級神經網絡模型

  • 通過實現深度學習算法和技術，學習執行各種任務

  • 在計算機視覺、自然語言處理、深度強化學習、可解釋人工智能等領域獲得專業知識

書籍描述

深度學習推動著人工智能的革命，而 PyTorch 讓任何人都能更輕鬆地構建深度學習應用。這本 PyTorch 書籍將幫助您揭示專家技巧，充分利用您的數據並構建複雜的神經網絡模型。

本書首先快速概述了 PyTorch，並探討了使用卷積神經網絡（CNN）架構進行圖像分類。然後，您將使用循環神經網絡（RNN）架構和 Transformer 進行情感分析。隨著進一步的學習，您將應用深度學習於不同領域，例如使用生成模型進行音樂、文本和圖像生成，並探索生成對抗網絡（GANs）的世界。您不僅將在 PyTorch 中構建和訓練自己的深度強化學習模型，還將使用專家技巧和技術將 PyTorch 模型部署到生產環境中。最後，您將掌握以分佈式方式高效訓練大型模型、使用 AutoML 有效搜索神經網絡架構以及使用 PyTorch 和 fast.ai 快速原型設計模型的技巧。

通過閱讀本書，您將能夠使用 PyTorch 執行複雜的深度學習任務，構建智能的人工智能模型。

您將學到什麼


  • 使用 PyTorch 實現文本和音樂生成模型

  • 在 PyTorch 中構建深度 Q 網絡（DQN）模型

  • 使用 Open Neural Network Exchange（ONNX）導出通用的 PyTorch 模型

  • 熟練使用 fast.ai 在 PyTorch 中進行快速原型設計

  • 使用 AutoML 有效地進行神經網絡架構搜索

  • 使用 Captum 輕鬆解釋在 PyTorch 中編寫的機器學習模型

  • 使用 PyTorch 設計 ResNets、LSTMs、Transformers 等

  • 了解如何使用 torch.distributed API 在 PyTorch 中進行分佈式訓練

適合閱讀對象

本書適合數據科學家、機器學習研究人員和深度學習從業者，他們希望使用 PyTorch 1.x 實現高級深度學習範例。需要具備使用 Python 編程進行深度學習的工作知識。

作者簡介

Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), his master's degree in computer science from EPFL (Switzerland), and an MBA degree from the Quantic School of Business (Washington). He received distinctions in all of his degrees. He has worked for a variety of tech companies, including Oracle and Sony, and tech start ups, such as Revolut, as a machine learning engineer.

Aside from his years of work experience, Ashish is a freelance ML consultant, an author, and a blogger (datashines). He has worked on products/projects ranging from using sensor data for predicting vehicle types to detecting fraud in insurance claims. In his spare time, Ashish works on open source ML projects and is active on StackOverflow and kaggle (arj7192).

作者簡介(中文翻譯)

Ashish Ranjan Jha在印度的IIT Roorkee獲得了電機工程學士學位，瑞士EPFL獲得了計算機科學碩士學位，並在華盛頓的Quantic商學院獲得了MBA學位。他在所有學位中都獲得了優異成績。他曾在多家科技公司工作，包括Oracle和Sony，以及Revolut等科技初創公司，擔任機器學習工程師。

除了多年的工作經驗外，Ashish還是一名自由職業機器學習顧問、作家和博主（datashines）。他曾參與各種產品/項目，從使用感測器數據預測車輛類型到檢測保險索賠中的欺詐行為。在閒暇時間，Ashish致力於開源機器學習項目，並在StackOverflow和kaggle（arj7192）上活躍。

目錄大綱

  1. Overview of Deep Learning Using PyTorch
  2. Combining CNNs and LSTMs
  3. Deep CNN Architectures
  4. Deep Recurrent Model Architectures
  5. Hybrid Advanced Models
  6. Music and Text Generation with PyTorch
  7. Neural Style Transfer
  8. Deep Convolutional GANs
  9. Deep Reinforcement Learning
  10. Operationalizing Pytorch Models into Production
  11. Distributed Training
  12. PyTorch and AutoML
  13. PyTorch and Explainable AI
  14. Rapid Prototyping with PyTorch

目錄大綱(中文翻譯)

深度學習使用PyTorch概述
結合CNN和LSTM
深度CNN架構
深度循環模型架構
混合高級模型
使用PyTorch進行音樂和文本生成
神經風格轉換
深度卷積GAN
深度強化學習
將PyTorch模型應用於生產環境
分散式訓練
PyTorch和AutoML
PyTorch和可解釋的AI
使用PyTorch進行快速原型設計

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