Deep Learning with Keras

Antonio Gulli, Sujit Pal

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商品描述

Key Features

  • Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games
  • See how various deep-learning models and practical use-cases can be implemented using Keras
  • A practical, hands-on guide with real-world examples to give you a strong foundation in Keras

Book Description

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer.

Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks.

What you will learn

  • Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm
  • Fine-tune a neural network to improve the quality of results
  • Use deep learning for image and audio processing
  • Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases
  • Identify problems

商品描述(中文翻譯)

主要特點:

- 在Keras中實現各種深度學習算法,並了解深度學習在遊戲中的應用
- 使用Keras實現各種深度學習模型和實際應用案例
- 通過實際示例提供實用的、實戰導向的指南,幫助您建立堅實的Keras基礎

書籍描述:

本書首先介紹了監督學習算法,如簡單的線性回歸、傳統的多層感知器以及更複雜的深度卷積網絡。您還將探索圖像處理,包括手寫數字圖像識別、圖像分類以及相關圖像標註的高級物體識別。書中還提供了人臉檢測中突出點識別的示例。接下來,您將介紹適用於處理序列數據(如文本、音頻或時間序列)的循環網絡。隨後,您將學習無監督學習算法,如自編碼器和非常流行的生成對抗網絡(GAN)。您還將探索神經網絡在風格轉換等非傳統用途中的應用。

最後,您將研究強化學習及其在人工智能遊戲中的應用,這是神經網絡研究和應用的另一個熱門方向。

您將學到什麼:

- 使用反向傳播算法逐步優化大型神經網絡的功能
- 進行微調以提高結果的質量
- 使用深度學習進行圖像和音頻處理
- 在特殊情況下使用遞歸神經張量網絡(RNTNs)優於標準詞嵌入
- 識別問題