Make Your Own Neural Network in Python

INTERACTIVE COURSE

Make Your Own Neural Network in Python

Beginner

83 Lessons

6h

Certificate of Completion

AI Explanations

AI Explanations

Make Your Own Neural Network in Python

41 Playgrounds

105 Illustrations

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

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

How You'll Learn

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