Hands-On Deep Learning Architectures with Python

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By Yuxi (Hayden) Liu , Saransh Mehta
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  1. Section 1: The Elements of Deep Learning

About this book

Deep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems.

Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations.

By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.

Publication date:
April 2019
Publisher
Packt
Pages
316
ISBN
9781788998086

 

Section 1: The Elements of Deep Learning

In this section, you will get an overview of deep learning with Python, and will also learn about the architectures of the deep feedforward network, the Boltzmann machine, and autoencoders. We will also practice examples based on DFN and applications of the Boltzmann machine and autoencoders, with the concrete examples based on the DL frameworks/libraries with Python, along with their benchmarks.

This section consists of the following chapters:

  • Chapter 1Getting Started with Deep Learning
  • Chapter 2Deep Feedforward Networks
  • Chapter 3Restricted Boltzmann Machines and Autoencoders

About the Authors

  • Yuxi (Hayden) Liu

    Yuxi (Hayden) Liu is a Software Engineer, Machine Learning at Google. He is developing and improving machine learning models and systems for ads optimization on the largest search engine in the world.

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  • Saransh Mehta

    Saransh Mehta has cross-domain experience of working with texts, images, and audio using deep learning. He has been building artificial, intelligence-based solutions, including a generative chatbot, an attendee-matching recommendation system, and audio keyword recognition systems for multiple start-ups. He is very familiar with the Python language, and has extensive knowledge of deep learning libraries such as TensorFlow and Keras. He has been in the top 10% of entrants to deep learning challenges hosted by Microsoft and Kaggle.

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