A Refresher Guide to Convolution Neural Networks
A Refresher Guide to Convolution Neural Networks
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A Refresher Guide to Convolution Neural Networks

This book is 100% complete

Completed on 2020-03-27

About the Book

The goal of any Convolution Neural Network is to learn higher-order features from data representation, achieving that via convolutions. This type of Neural Nets is very good in dealing with tensor data such as images and is well suited to object recognition with consistently top image classification competitions. In this part, I will try to teach you the convolution neural network on weekend.

An online and free version of this part can be found here.

You can check the Scratching Linear Algebra in Weekend here.

About the Author

Hisham El-Amir
Hisham El-Amir

Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

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Scratching Linear Algebra in Weekend
A Refresher Guide to Convolution Neural Networks
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Table of Contents

  1. Background
  2. Table of Content
  3. Introduction
  4. Inspiration
  5. Intuition
  6. CNN Architecture Overview
    1. Neuron spatial arrangements
    2. Evolution of the connections between layers
  7. Input Layers
  8. Convolution Layers
    1. Convolution
    2. Filters
    3. Activation maps
    4. Parameter sharing
    5. Learned filters and renders
    6. ReLU activation functions as layers
    7. Convolution layer hyper-parameters
    8. Batch normalization and layers
  9. Pooling Layer
  10. Fully Connected Layer
  11. Other Applications of CNNs
  12. CNNs of Note
  13. Summary

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