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Educative – Make Your Own Neural Network in Python
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Prologue
The Search for Intelligent MachinesPreview
A Nature Inspired New Golden AgePreview
Introduction
Who is this course for?Preview
What will we do?Preview
How will we do it?Preview
Author’s NotePreview
Part 1 – A Little Background
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
Part 2 – Let’s Get Started!
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
Part 3 – Backward Propagation of Error
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
Part 4 – Adjusting the Link Weights
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
Part 5 – A Gentle Start with Python
Getting Started
Loops
Functions
Arrays
Plotting Arrays
Objects
Methods
Part 6 – Neural Network with Python
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
Part 7 – Testing Neural Network against MNIST Dataset
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
Part 8 – Some Suggested Improvements
Tweaking the Learning Rate
Doing Multiple Runs
Change Network Shape
Part 9 – Even More Fun!
Your Own Handwriting
Inside the Mind of a Neural Network
Backward Query
More Brain Scans
Creating New Training Data by Rotations
Epilogue
Epilogue
Appendix: A Small Guide to Calculus
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
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