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Make Your Own Neural Network in Python

Make Your Own Neural Network in Python

Beginner

83 Lessons

6h

Certificate of Completion

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Explanations

AI-POWERED

Explanations

This course includes

41 Playgrounds

This course includes

41 Playgrounds
Course Overview
Course Content

Course Overview

Machine learning is one of the fastest growing fields, and we cannot emphasize enough about its importance. This course aims to teach one of the fundamental concepts of machine learning, i.e., Neural Network. You will learn the basic concepts of building a model as well as the mathematical explanation behind Neural Network and based on that; you will build one from scratch (in Python). You will also learn how to train and optimize your network to achieve a better result.

Course Content

1.

Prologue

The Search for Intelligent Machines
A Nature Inspired New Golden Age
Introduction
2.

A Little Background

Humans vs. Computers
A Simple Prediction Machine
Estimate the Constant Iteratively
Classify vs. Predict
Build a Simple Classifier
Errors in the Training Classifier
Refine the Parameters of the Training Classifier
Set Up a Learning Rate in the Training Classifier
Limitations of Linear Classifiers
Represent Boolean Functions with Linear Classification
3.

Let's Get Started!

Neurons: Nature’s Computing Machines
How Neurons Work
Activation Functions
Replicate Neurons in an Artificial Model
Follow Signals through a Simple Network
Calculate Neural Network Output
Matrix Multiplication
Calculate the Inputs for Internal Layers
A Three-Layer Example: Working on the Input Layer
A Three-Layer Example: Working on the Hidden Layer
A Three-Layer Example: Working on the Output Layer
4.

Backward Propagation of Error

Learn Weights from More than One Node
Error Backpropagation from More Output Nodes
Backpropagation: Splitting the Error
Backpropagation: Recombine the Error
Error Backpropagation with Matrix Multiplication
5.

Adjusting the Link Weights

Update Weights
Embrace Pessimism
Gradient Descent Algorithm
Transform the Output into an Error Function
Use Gradient Descent to Update Weights
Choose the Right Weights Iteratively
The Error Slope between the Input and Hidden Layers
Weight Updates Calculated
Prepare Data: Inputs and Outputs
Prepare Data: Random Initial Weights
6.

A Gentle Start with Python

7 Lessons

7.

Neural Network with Python

8 Lessons

8.

Testing Neural Network against MNIST Dataset

11 Lessons

9.

Some Suggested Improvements

4 Lessons

10.

Even More Fun!

4 Lessons

11.

Epilogue

1 Lesson

12.

Appendix: A Small Guide to Calculus

9 Lessons

Course Author

Practice and apply your skills

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Anthony Walker

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Evan Dunbar

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Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Souvik Kundu

Front-end Developer

Eric Downs

Musician/Entrepeneur

Anthony Walker

@_webarchitect_

Evan Dunbar

ML Engineer

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