Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python
暫譯: 應用深度學習與 TensorFlow 2：學習使用 Python 實現進階深度學習技術

Michelucci, Umberto

Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python
  • 出版商: Apress
  • 出版日期: 2022-03-29
  • 售價: $2,550
  • 貴賓價: 9.5 折 $2,423
  • 語言: 英文
  • 頁數: 410
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1484280199
  • ISBN-13: 9781484280195
  • 相關分類: Python、程式語言、DeepLearning、TensorFlow

    • 海外代購書籍(需單獨結帳)

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商品描述

Understand how neural networks work and learn how to implement them using TensorFlow 2.0 and Keras. This new edition focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects.

This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks.

All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally.

You will: 

• Understand the fundamental concepts of how neural networks work
• Learn the fundamental ideas behind autoencoders and generative adversarial networks
• Be able to try all the examples with complete code examples that you can expand for your own projects
• Have available a complete online companion book with examples and tutorials.

This book is for:
Readers with an intermediate understanding of machine learning, linear algebra, calculus, and basic Python programming. 

商品描述(中文翻譯)

了解神經網絡的運作原理，並學習如何使用 TensorFlow 2.0 和 Keras 實現它們。本書的新版本專注於基本概念，同時也涵蓋了實現神經網絡和深度學習在研究項目中的實際應用。

本書的設計使您可以專注於您感興趣的部分。您將探索正則化、優化器、優化、度量分析和超參數調整等主題。此外，您還將學習自編碼器（autoencoders）和生成對抗網絡（generative adversarial networks）背後的基本概念。

書中呈現的所有代碼將以 Jupyter notebooks 的形式提供，這將使您能夠嘗試所有示例並以有趣的方式擴展它們。還提供了一本伴隨的在線書籍，其中包含書中討論的所有示例的完整代碼以及與 TensorFlow 和 Keras 更相關的附加材料。所有代碼將以 Jupyter notebook 格式提供，可以直接在 Google Colab 中打開（無需在本地安裝任何東西），或下載到您自己的機器上進行本地測試。

您將：
- 了解神經網絡運作的基本概念
- 學習自編碼器和生成對抗網絡背後的基本思想
- 能夠嘗試所有示例，並擁有完整的代碼示例，您可以擴展以用於自己的項目
- 獲得一本完整的在線伴隨書籍，內含示例和教程。

本書適合：
對機器學習、線性代數、微積分和基本 Python 編程有中級理解的讀者。

作者簡介

Umberto Michelucci is the founder and the chief AI scientist of TOELT – Advanced AI LAB LLC. He’s an expert in numerical simulation, statistics, data science, and machine learning. He has 15 years of practical experience in the fields of data warehouse, data science, and machine learning. His first book, Applied Deep Learning―A Case-Based Approach to Understanding Deep Neural Networks, was published in 2018. His second book, Convolutional and Recurrent Neural Networks Theory and Applications was published in 2019. He publishes his research regularly and gives lectures on machine learning and statistics at various universities. He holds a PhD in machine learning, and he is also a Google Developer Expert in Machine Learning based in Switzerland.

作者簡介(中文翻譯)

Umberto Michelucci 是 TOELT – Advanced AI LAB LLC 的創辦人及首席 AI 科學家。他在數值模擬、統計學、數據科學和機器學習方面是專家。他在數據倉儲、數據科學和機器學習領域擁有 15 年的實務經驗。他的第一本書《Applied Deep Learning―A Case-Based Approach to Understanding Deep Neural Networks》於 2018 年出版。第二本書《Convolutional and Recurrent Neural Networks Theory and Applications》於 2019 年出版。他定期發表研究成果，並在各大學講授機器學習和統計學。他擁有機器學習的博士學位，並且是位於瑞士的 Google Developer Expert in Machine Learning。

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