Hands-On Deep Learning Algorithms with Python

Ravichandiran, Sudharsan

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Learn
  • 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
About

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.

Features
  • 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

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學習


  • 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





關於

深度學習是人工智慧領域中最受歡迎的領域之一,它允許您開發不同複雜性的多層模型。

 

本書介紹了流行的深度學習演算法,從基礎到高級,並展示了如何使用TensorFlow從頭實現這些演算法。在整本書中,您將獲得每個演算法的洞察力,涉及的數學原理以及如何以最佳方式實現它。本書首先解釋了如何構建自己的神經網絡,然後介紹了TensorFlow,這是一個強大的基於Python的機器學習和深度學習庫。接著,您將了解梯度下降的變體,如NAG、AMSGrad、AdaDelta、Adam和Nadam。本書還將為您提供關於循環神經網絡(RNN)和LSTM的洞察力,以及如何使用RNN生成歌詞。接下來,您將掌握處理卷積和膠囊網絡所需的數學知識,這在圖像識別任務中被廣泛使用。您還將學習機器如何使用CBOW、skip-gram和PV-DM理解詞語和文檔的語義。最後,您將探索GAN,包括InfoGAN和LSGAN,以及自編碼器,如壓縮自編碼器和VAE。

 

通過閱讀本書,您將具備在自己的項目中實現深度學習所需的所有技能。





特點


  • 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