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Deep Learning Essentials

You're reading from   Deep Learning Essentials Your hands-on guide to the fundamentals of deep learning and neural network modeling

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Product type Paperback
Published in Jan 2018
Publisher Packt
ISBN-13 9781785880360
Length 284 pages
Edition 1st Edition
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Authors (3):
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Wei Di Wei Di
Author Profile Icon Wei Di
Wei Di
Anurag Bhardwaj Anurag Bhardwaj
Author Profile Icon Anurag Bhardwaj
Anurag Bhardwaj
Jianing Wei Jianing Wei
Author Profile Icon Jianing Wei
Jianing Wei
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Toc

Table of Contents (12) Chapters Close

Preface 1. Why Deep Learning? FREE CHAPTER 2. Getting Yourself Ready for Deep Learning 3. Getting Started with Neural Networks 4. Deep Learning in Computer Vision 5. NLP - Vector Representation 6. Advanced Natural Language Processing 7. Multimodality 8. Deep Reinforcement Learning 9. Deep Learning Hacks 10. Deep Learning Trends 11. Other Books You May Enjoy

Origins of CNNs

Walter Pitts and Warren McCulloch are often credited with the first computer model in 1943, which was inspired by the neural network-based structure of the human brain. They proposed a technique that inspired the notion of logic-based design and provided a formalism under which future refinements led to the invention of Finite Automata. The McCulloch-Pitts network was a directed graph where each node was a neuron and edges were marked as either excitatory (1) or inhibitory (0), and used a threshold logic to replicate the human thought process.

One of the challenges in this design was the learning of thresholds or weights, as would be defined later. Henry J. Kelley provided the first version of this learning algorithm in the form of a continuous backpropagation model in 1960 followed by an improvement by Arthur Bryson. The chain rule was developed by Stuart Dreyfus...

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