Deep Learning with TensorFlow and Keras, 3/e (Paperback)
暫譯: 使用 TensorFlow 和 Keras 的深度學習(第三版)

Kapoor, Amita, Gulli, Antonio, Pal, Sujit

  • 出版商: Packt Publishing
  • 出版日期: 2022-10-06
  • 售價: $1,980
  • 貴賓價: 9.5$1,881
  • 語言: 英文
  • 頁數: 698
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1803232919
  • ISBN-13: 9781803232911
  • 相關分類: DeepLearningReinforcementTensorFlow
  • 立即出貨 (庫存 < 3)

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商品描述

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.

商品描述(中文翻譯)

建立尖端的機器學習和深度學習系統,適用於實驗室、生產和行動裝置

主要特點

- 透過清晰的解釋和廣泛的程式碼範例,了解深度學習和機器學習的基本原理
- 使用 Hugging Face 和 TensorFlow Hub 實作圖神經網絡、變壓器,以及聯合學習和對比學習
- 學習尖端的機器學習和深度學習技術

書籍描述

《使用 TensorFlow 和 Keras 的深度學習》教你使用 TensorFlow (TF) 和 Keras 的神經網絡和深度學習技術。你將學會如何在最強大、最受歡迎且可擴展的機器學習堆疊中編寫深度學習應用程式。

TensorFlow 2.x 專注於簡單性和易用性,更新包括即時執行、基於 Keras 的直觀高階 API,以及在任何平台上靈活的模型構建。本書使用最新的 TF 2.0 特性和庫,概述監督式和非監督式機器學習模型,並提供使用實際範例進行深度學習和強化學習模型的全面分析,適用於雲端、行動和大型生產環境。

本書還展示了如何使用 TensorFlow 創建神經網絡,涵蓋流行的演算法(回歸、卷積神經網絡 (CNNs)、變壓器、生成對抗網絡 (GANs)、遞迴神經網絡 (RNNs)、自然語言處理 (NLP) 和圖神經網絡 (GNNs)),並提供工作範例應用程式,然後深入探討 TF 在生產中的應用、TF 行動和 TensorFlow 與 AutoML 的結合。

你將學到的內容

- 學習如何使用流行的 GNNs 與 TensorFlow 執行圖挖掘任務
- 探索變壓器的世界,從預訓練到微調再到評估
- 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 結合概率模型和深度學習模型
- 在雲端訓練你的模型,並在實際環境中運用 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

這本實作導向的機器學習書籍適合希望使用 TensorFlow 構建機器學習和深度學習系統的 Python 開發者和數據科學家。本書提供使用 Keras、TensorFlow 和 AutoML 構建機器學習系統所需的理論和實踐。

一些機器學習的知識會有幫助。我們不假設讀者具備 TF 知識。

目錄大綱

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

目錄大綱(中文翻譯)

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