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Deep Learning By Example

You're reading from   Deep Learning By Example A hands-on guide to implementing advanced machine learning algorithms and neural networks

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788399906
Length 450 pages
Edition 1st Edition
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Author (1):
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Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
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Table of Contents (18) Chapters Close

Preface 1. Data Science - A Birds' Eye View 2. Data Modeling in Action - The Titanic Example FREE CHAPTER 3. Feature Engineering and Model Complexity – The Titanic Example Revisited 4. Get Up and Running with TensorFlow 5. TensorFlow in Action - Some Basic Examples 6. Deep Feed-forward Neural Networks - Implementing Digit Classification 7. Introduction to Convolutional Neural Networks 8. Object Detection – CIFAR-10 Example 9. Object Detection – Transfer Learning with CNNs 10. Recurrent-Type Neural Networks - Language Modeling 11. Representation Learning - Implementing Word Embeddings 12. Neural Sentiment Analysis 13. Autoencoders – Feature Extraction and Denoising 14. Generative Adversarial Networks 15. Face Generation and Handling Missing Labels 16. Implementing Fish Recognition 17. Other Books You May Enjoy

Capacity of a single neuron

A neural network is a computational model that is mainly inspired by the way the biological neural networks of the human brain process the incoming information. Neural networks made a huge breakthrough in machine learning research (deep learning, specifically) and industrial applications, such as breakthrough results in computer vision, speech recognition, and text processing. In this chapter, we will try to develop an understanding of a particular type of neural network called the multi-layer Perceptron.

Biological motivation and connections

The basic computational unit of our brains is called a neuron, and we have approximately 86 billion neurons in our nervous system, which are connected with...

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