Educative – Make Your Own Neural Network in Python

The Search for Intelligent MachinesPreview

A Nature Inspired New Golden AgePreview

Who is this course for?Preview

What will we do?Preview

How will we do it?Preview

Author’s NotePreview

Easy for Me, Hard for YouPreview

A Simple Predicting MachinePreview

Estimating the Constant “c” Iteratively

Classifying vs. Predicting

Building a Simple Classifier

Error in the Training Classifier

Refining the Parameters of Training Classifier

Setting up Learning Rate in Training Classifier

Limitations of Linear Classifiers

Representing Boolean Functions with Linear Classification

Neurons, Nature’s Computing MachinesPreview

How Neurons Really Work?

What is an Activation Function?

Replicating Neuron to an Artificial Model

Following Signals Through A Simpler Network

Calculating Neural Network Output

Matrix Multiplication is Useful .. Honest!

Calculating Inputs for Internal Layers

A Three Layer Example: Working on Input Layer

A Three Layer Example: Working on Hidden Layer

A Three Layer Example: Working on Output Layer

Learning Weights From More Than One Node

Backpropagating Errors From More Output Nodes

Backpropagation: Splitting the Error

Backpropagation: Recombining the Error

Backpropagating Errors with Matrix Multiplication

How Do We Actually Update Weights?

Embrace Pessimism

Understanding the Gradient Descent Algorithm

How to Transform the Output into Error Function?

Using Gradient Descent to Update Weights

Choosing the Right Weights…Iteratively!

One Last Thing…

Weight Update Worked Example

Preparing Data: Inputs & Outputs

Preparing Data: Random Initial Weights

Getting Started

Loops

Functions

Arrays

Plotting Arrays

Objects

Methods

Building the Neural Network Class

Initializing the Network

Weights – The Heart of the Network

Optional: More Sophisticated Weights

Querying the Network

Applying Sigmoid Function

The Code Thus Far..

Testing Our Code Thus Far

Training the Network

Refining the Weights

The Complete Neural Network Code

The MNIST Dataset of Handwritten Numbers

A Quick Look at the Data Files

Getting the Dataset Ready

Plotting the Data Points

Preparing the MNIST Training Data

The Need to Rescale the Target Output

Python Code to Create and Rescale the Output Array

Updating Neural Network Code

Testing the Network on a Subset

Testing the Network Against the Whole Dataset!

Updating the Neural Network Code…Again

Tweaking the Learning Rate

Doing Multiple Runs

Change Network Shape

Your Own Handwriting

Inside the Mind of a Neural Network

Backward Query

More Brain Scans

Creating New Training Data by Rotations

Epilogue

A Gentle Introduction

A Flat Line

A Sloped Straight Line

A Curved Line

Calculus By Hand

Calculus Not By Hand

Calculus without Plotting Graphs

Patterns

Functions of Functions

Handling Independent Variables