Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python

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. 

了解神經網絡的工作原理，並學習如何使用TensorFlow 2.0和Keras實現它們。這本新版書籍專注於基本概念以及在研究項目中實現神經網絡和深度學習的實際方面。

本書設計成讓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和生成對抗網絡背後的基本思想。

書中呈現的所有代碼將以Jupyter筆記本的形式提供，這將使您能夠嘗試所有示例並以有趣的方式擴展它們。還提供了一本在線附屬書，其中包含書中討論的所有示例的完整代碼以及更多與TensorFlow和Keras相關的材料。所有代碼都以Jupyter筆記本格式提供，可以直接在Google Colab中打開（無需在本地安裝任何軟件），或者下載到您自己的計算機上並在本地測試。

您將會：
- 理解神經網絡的基本概念
- 學習自編碼器和生成對抗網絡的基本思想
- 能夠嘗試所有示例，並使用完整的代碼示例擴展您自己的項目
- 可以獲得一本完整的在線附屬書，其中包含示例和教程。

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。

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.

Umberto Michelucci 是 TOELT - Advanced AI LAB LLC 的創始人和首席人工智慧科學家。他是數值模擬、統計、資料科學和機器學習方面的專家。他在數據倉庫、資料科學和機器學習領域擁有15年的實踐經驗。他的第一本書《應用深度學習-基於案例的理解深度神經網絡》於2018年出版。他的第二本書《卷積和循環神經網絡理論與應用》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。他擁有機器學習博士學位，也是一位在瑞士的 Google 機器學習開發專家。