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

A Part of Weekend Series

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.

Also, do not forget to check the following weekends:

Python Basics

Python OOP


Pandas I.



Introduction to Data

Data Visualization and Understanding

Data Cleaning and Preprocessing Techniques.

Data Exploration and Analysis.

Data Exploration Part I.

Data Exploration Part II.

Data Exploration, It's all about Data Wrangling.

The Art of Data Visualization.

Rules of data visualization.

Linear Algebra.

Math for Machine Learning.

Gradient Descent.

Linear Regression.

Logistic Regression.

Support Vector Machines.

Tree Methods.

Ensemble Methods.

Introduction to Deep Learning.

Vanilla Neural Network, A refresher guide.

Convolution Neural Network, A refresher guide.

Recurrent Neural Network, A refresher guide.

Attention Mechanism, A refresher guide.

Auto Encoders, A refresher guide.

Generative Adversarial Networks, A refresher guide.

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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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Reader Testimonials

Shehzada (Shaz-z)
Shehzada (Shaz-z)

Its starts with a good introduction to CNN, relating with biological inspiration, describing how it's not only meant for an image related task but also in NLP with 1D CNN operation. Visually showcasing the CNN architecture, describing parameter sharing, the progression of CNN architectures overtime. Few more things can be included like types of convolution operations (Dilated Convolution and Transposed convolution) and how 1D CNN works in doing NLP, it will be great if you could add in the next

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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