Hands-On Deep Learning Algorithms with Python

Ravichandiran, Sudharsan


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  • Implement basic-to-advanced deep learning algorithms
  • Master the mathematics behind deep learning algorithms
  • Become familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and Nadam
  • Implement recurrent networks, such as RNN, LSTM, GRU, and seq2seq models
  • Understand how machines interpret images using CNN and capsule networks
  • Implement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGAN
  • Explore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAE

Deep learning is one of the most popular domains in the AI space that allows you to develop multi-layered models of varying complexities.


This book introduces you to popular deep learning algorithms—from basic to advanced—and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles involved, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into recurrent neural networks (RNNs) and LSTM and how to generate song lyrics with RNN. Next, you will master the math necessary to work with convolutional and capsule networks, widely used for image recognition tasks. You will also learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Finally, you will explore GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE.


By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.

  • Get up to speed with building your own neural networks from scratch
  • Gain insights into the mathematical principles behind deep learning algorithms
  • Implement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlow




  • 實現基礎到高級的深度學習演算法

  • 掌握深度學習演算法背後的數學原理

  • 熟悉梯度下降及其變體,如AMSGrad、AdaDelta、Adam和Nadam

  • 實現循環網絡,如RNN、LSTM、GRU和seq2seq模型

  • 了解機器如何使用CNN和膠囊網絡解釋圖像

  • 實現不同類型的生成對抗網絡,如CGAN、CycleGAN和StackGAN

  • 探索各種類型的自編碼器,如稀疏自編碼器、DAE、CAE和VAE








  • 從頭開始構建自己的神經網絡

  • 深入了解深度學習演算法背後的數學原理

  • 使用TensorFlow實現流行的深度學習演算法,如CNN、RNN等