Computational Methods for Deep Learning: Theoretic, Practice and Applications

Computational Methods for Deep Learning: Theoretic, Practice and Applications
ePUB
  • eBook:
    Computational Methods for Deep Learning: Theoretic, Practice and Applications
  • Author:
    Wei Qi Yan
  • Edition:
    1st ed. 2021 edition
  • Categories:
  • Data:
    December 5, 2020
  • ISBN:
    3030610802
  • ISBN-13:
    9783030610807
  • Language:
    English
  • Pages:
    151 pages
  • Format:
    ePUB

Book Description
Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations.
Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms.
As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers.
This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision.

Content

1. Introduction
2. Deep Learning Platforms
3. CNN and RNN
4. Autoencoder and GAN
5. Reinforcement Learning
6. CapsNet and Manifold Learning
7. Boltzmann Machines
8. Transfer Learning and Ensemble Learning

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