Deep Learning with TensorFlow and Keras, 3/e (Paperback)

Build cutting edge machine and deep learning systems for the lab, production, and mobile devices

Key Features

- Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples
- Implement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learning
- Learn cutting-edge machine and deep learning techniques

Book Description

Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.

TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.

This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.

What you will learn

- Learn how to use the popular GNNs with TensorFlow to carry out graph mining tasks
- Discover the world of transformers, from pretraining to fine-tuning to evaluating them
- Apply self-supervised learning to natural language processing, computer vision, and audio signal processing
- Combine probabilistic and deep learning models using TensorFlow Probability
- Train your models on the cloud and put TF to work in real environments
- Build machine learning and deep learning systems with TensorFlow 2.x and the Keras API

Who this book is for

This hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems.

Some machine learning knowledge would be useful. We don't assume TF knowledge.

建立先進的機器學習和深度學習系統，適用於實驗室、生產和移動設備。

主要特點:
- 通過清晰的解釋和大量的代碼示例，了解深度學習和機器學習的基礎知識。
- 使用Hugging Face和TensorFlow Hub實現圖神經網絡、Transformer以及聯合和對比學習。
- 學習尖端的機器學習和深度學習技術。

書籍描述:
《Deep Learning with TensorFlow and Keras》教授使用TensorFlow（TF）和Keras的神經網絡和深度學習技術。您將學習如何在最強大、最受歡迎和可擴展的機器學習堆棧中編寫深度學習應用程序。

TensorFlow 2.x注重簡單性和易用性，更新包括即時執行、基於Keras的直觀高級API和在任何平台上靈活構建模型。本書使用最新的TF 2.0功能和庫，概述了監督和非監督機器學習模型，並提供了深度學習和強化學習模型的全面分析，並提供了雲端、移動和大型生產環境的實用示例。

本書還向您展示如何使用TensorFlow創建神經網絡，介紹了流行的算法（回歸、卷積神經網絡（CNN）、Transformer、生成對抗網絡（GAN）、循環神經網絡（RNN）、自然語言處理（NLP）和圖神經網絡（GNN）），涵蓋了實際應用程序示例，然後深入探討了在生產環境中的TF、TF移動和TensorFlow與AutoML。

您將學到:
- 學習如何使用TensorFlow中的流行GNN進行圖形挖掘任務。
- 從預訓練到微調再到評估，探索Transformer的世界。
- 將自我監督學習應用於自然語言處理、計算機視覺和音頻信號處理。
- 使用TensorFlow Probability結合概率和深度學習模型。
- 在雲端上訓練模型，並在實際環境中應用TF。
- 使用TensorFlow 2.x和Keras API構建機器學習和深度學習系統。

本書適合對象:
本實踐機器學習書籍適用於希望使用TensorFlow構建機器學習和深度學習系統的Python開發人員和數據科學家。本書為您提供了使用Keras、TensorFlow和AutoML構建機器學習系統所需的理論和實踐。

一些機器學習知識將會有所幫助，我們不假設讀者具備TensorFlow知識。

1. Neural Networks Foundations with TF
2. Regression and Classification
3. Convolutional Neural Networks
4. Word Embeddings
5. Recurrent Neural Network
6. Transformers
7. Unsupervised Learning
8. Autoencoders
9. Generative Models
10. Self-Supervised Learning
11. Reinforcement Learning
12. Probabilistic TensorFlow
13. An Introduction to AutoML
14. The Math Behind Deep Learning
15. Tensor Processing Unit
16. Other Useful Deep Learning Libraries
17. Graph Neural Networks
18. Machine Learning Best Practices
19. TensorFlow 2 Ecosystem
20. Advanced Convolutional Neural Networks

1. 使用 TensorFlow 的神經網絡基礎
2. 迴歸和分類
3. 卷積神經網絡
4. 詞嵌入
5. 遞歸神經網絡
6. 轉換器
7. 非監督學習
8. 自編碼器
9. 生成模型
10. 自監督學習
11. 強化學習
12. TensorFlow 的概率
13. 自動機器學習簡介
14. 深度學習背後的數學
15. 張量處理單元
16. 其他有用的深度學習庫
17. 圖神經網絡
18. 機器學習最佳實踐
19. TensorFlow 2 生態系統
20. 高級卷積神經網絡