Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow, 2nd Edition
暫譯: Python 深度學習:使用 PyTorch、Keras 和 TensorFlow 探索深度學習技術與神經網絡架構(第二版)

Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca

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商品描述

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries

Key Features

  • Build a strong foundation in neural networks and deep learning with Python libraries
  • Explore advanced deep learning techniques and their applications across computer vision and NLP
  • Learn how a computer can navigate in complex environments with reinforcement learning

Book Description

With the surge in artificial intelligence in applications catering to both business and consumer needs, deep learning is more important than ever for meeting current and future market demands. With this book, you'll explore deep learning, and learn how to put machine learning to use in your projects.

This second edition of Python Deep Learning will get you up to speed with deep learning, deep neural networks, and how to train them with high-performance algorithms and popular Python frameworks. You'll uncover different neural network architectures, such as convolutional networks, recurrent neural networks, long short-term memory (LSTM) networks, and capsule networks. You'll also learn how to solve problems in the fields of computer vision, natural language processing (NLP), and speech recognition. You'll study generative model approaches such as variational autoencoders and Generative Adversarial Networks (GANs) to generate images. As you delve into newly evolved areas of reinforcement learning, you'll gain an understanding of state-of-the-art algorithms that are the main components behind popular games Go, Atari, and Dota.

By the end of the book, you will be well-versed with the theory of deep learning along with its real-world applications.

What you will learn

  • Grasp the mathematical theory behind neural networks and deep learning processes
  • Investigate and resolve computer vision challenges using convolutional networks and capsule networks
  • Solve generative tasks using variational autoencoders and Generative Adversarial Networks
  • Implement complex NLP tasks using recurrent networks (LSTM and GRU) and attention models
  • Explore reinforcement learning and understand how agents behave in a complex environment
  • Get up to date with applications of deep learning in autonomous vehicles

Who this book is for

This book is for data science practitioners, machine learning engineers, and those interested in deep learning who have a basic foundation in machine learning and some Python programming experience. A background in mathematics and conceptual understanding of calculus and statistics will help you gain maximum benefit from this book.

Table of Contents

  1. Machine Learning – An Introduction
  2. Neural Networks
  3. Deep Learning Fundamentals
  4. Computer Vision With Convolutional Networks
  5. Advanced Computer Vision
  6. Generating images with GANs and Variational Autoencoders
  7. Recurrent Neural Networks and Language Models
  8. Reinforcement Learning Theory
  9. Deep Reinforcement Learning for Games
  10. Deep Learning in Autonomous Vehicles

商品描述(中文翻譯)

學習先進的最先進深度學習技術及其在流行 Python 函式庫中的應用

主要特點


  • 使用 Python 函式庫建立神經網絡和深度學習的堅實基礎

  • 探索先進的深度學習技術及其在計算機視覺和自然語言處理 (NLP) 中的應用

  • 學習計算機如何在複雜環境中進行導航,使用強化學習

書籍描述

隨著人工智慧在滿足商業和消費者需求的應用中激增,深度學習在滿足當前和未來市場需求方面變得比以往任何時候都更重要。本書將帶您探索深度學習,並學習如何在您的項目中運用機器學習。

這本《Python 深度學習》的第二版將使您迅速掌握深度學習、深度神經網絡及如何使用高效能算法和流行的 Python 框架來訓練它們。您將揭示不同的神經網絡架構,例如卷積網絡、遞歸神經網絡、長短期記憶 (LSTM) 網絡和膠囊網絡。您還將學習如何解決計算機視覺、自然語言處理 (NLP) 和語音識別領域的問題。您將研究生成模型方法,例如變分自編碼器和生成對抗網絡 (GANs) 來生成圖像。隨著您深入探索新演變的強化學習領域,您將了解流行遊戲 Go、Atari 和 Dota 背後的最先進算法。

在本書結束時,您將熟悉深度學習的理論及其在現實世界中的應用。

您將學到什麼


  • 掌握神經網絡和深度學習過程背後的數學理論

  • 使用卷積網絡和膠囊網絡調查和解決計算機視覺挑戰

  • 使用變分自編碼器和生成對抗網絡解決生成任務

  • 使用遞歸網絡 (LSTM 和 GRU) 和注意力模型實現複雜的 NLP 任務

  • 探索強化學習並了解代理在複雜環境中的行為

  • 了解深度學習在自駕車中的應用

本書適合誰

本書適合數據科學從業者、機器學習工程師以及對深度學習感興趣的讀者,前提是他們具備基本的機器學習基礎和一些 Python 編程經驗。具備數學背景以及對微積分和統計的概念理解將幫助您從本書中獲得最大收益。

目錄


  1. 機器學習 – 介紹

  2. 神經網絡

  3. 深度學習基礎

  4. 使用卷積網絡的計算機視覺

  5. 進階計算機視覺

  6. 使用 GANs 和變分自編碼器生成圖像

  7. 遞歸神經網絡和語言模型

  8. 強化學習理論

  9. 遊戲中的深度強化學習

  10. 自駕車中的深度學習