Deep Learning: Technical Introduction
by Thomas Epelbaum
Publisher: arXiv.org 2017
Number of pages: 106
This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent. For each network, their fundamental building blocks are detailed. The forward pass and the update rules for the backpropagation algorithm are then derived in full.
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by Juergen Schmidhuber - arXiv
In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium.
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This book can be useful for the university students learning about machine learning and the practitioners of machine learning, artificial intelligence, data-mining and data science aiming to better understand and take advantage of deep learning.
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