Pro Deep Learning with Tensorflow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python

This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0.

Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You'll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you'll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE.

Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications.

What You Will Learn

• Understand full-stack deep learning using TensorFlow 2.0
• Gain an understanding of the mathematical foundations of deep learning
• Deploy complex deep learning solutions in production using TensorFlow 2.0
• Understand generative adversarial networks, graph attention networks, and GraphSAGE

Who This Book Is For:

Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts.

本書在第一版的基礎上進行了擴充，更新了章節並使用最新的程式碼實現，以使其與Tensorflow 2.0保持最新。

《Pro Deep Learning with TensorFlow 2.0》首先介紹了深度學習的數學和核心技術基礎。接下來，您將學習卷積神經網絡，包括新的卷積方法，如膨脹卷積、深度可分離卷積及其實現方式。然後，您將了解在高級網絡架構中的自然語言處理，例如transformers和與自然語言處理和神經網絡相關的各種注意機制。隨著閱讀的進展，您將探索反映當前深度學習方法的無監督學習框架，如自編碼器和變分自編碼器。最後一章涵蓋了生成對抗網絡及其變體的高級主題，例如循環一致性GAN和圖神經網絡技術，如圖注意力網絡和GraphSAGE。

閱讀完本書後，您將了解深度學習的數學基礎和概念，並能夠使用示範的原型來構建新的深度學習應用程序。

本書的學習重點包括：
- 使用TensorFlow 2.0進行全棧深度學習
- 理解深度學習的數學基礎
- 使用TensorFlow 2.0在生產環境中部署複雜的深度學習解決方案
- 理解生成對抗網絡、圖注意力網絡和GraphSAGE

本書適合數據科學家、機器學習專業人士、軟件開發人員、研究生和開源愛好者閱讀。

Santanu Pattanayak works as a Senior Staff Machine Learning Specialist at Qualcomm Corp R&D and is the author of Quantum Machine Learning with Python, published by Apress. He has more than 16 years of experience, having worked at GE, Capgemini, and IBM before joining Qualcomm. He graduated with a degree in electrical engineering from Jadavpur University, Kolkata and is an avid math enthusiast. Santanu has a master's degree in data science from the Indian Institute of Technology (IIT), Hyderabad. He also participates in Kaggle competitions in his spare time, where he ranks in the top 500. Currently, he resides in Bangalore with his wife.

Santanu Pattanayak在高通公司研發部門擔任高級機器學習專家，並且是Apress出版的《Python量子機器學習》一書的作者。在加入高通之前，他在GE、Capgemini和IBM工作了超過16年。他畢業於加爾各答的Jadavpur大學，獲得電機工程學位，同時也是一位熱衷於數學的愛好者。Santanu在印度理工學院（IIT）海得拉巴分校獲得了數據科學碩士學位。他在業餘時間還參加Kaggle競賽，在其中排名前500名。目前，他與妻子居住在班加羅爾。