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Prologue
The Search for Intelligent Machines
A Nature Inspired New Golden Age
Introduction
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
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
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
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
A Gentle Start with Python
An Introduction to Python
Loops
Functions
Arrays
Plot the Arrays
Objects
Methods
Neural Network with Python
Build the Neural Network Class
Initialize the Neural Network
Weights: The Heart of the Network
Query the Network
Apply the Sigmoid Function
Python Code for a Basic Neural Network
Train the Network
Complete Code for a Neural Network
Testing Neural Network against MNIST Dataset
The MNIST Dataset
MNIST Data File
Get the Dataset Ready
Plot the Data Points
Prepare the MNIST Training Data
Rescale the Target Output
Create a Target Array
Update the Neural Network Code
Test the Network on a Subset
Test the Network on the Whole Dataset
Evaluate the Performance of the Neural Network
Some Suggested Improvements
Tweak the Learning Rate
Do Multiple Runs
Change the Network Shape
The Final Neural Network for MNIST Dataset
Even More Fun!
Query on Our Own Handwriting
Inside the Mind of a Neural Network
More Brain Scans
Create New Training Data with Rotations
Epilogue
Conclusion
Appendix: A Small Guide to Calculus
A Gentle Introduction to Calculus
A Flat Line
A Sloped Straight Line
A Curved Line
Calculus Not by Hand
Calculus without Plotting Graphs
Patterns
Chain Rule
Handling Independent Variables
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Make Your Own Neural Network in Python
A Gentle Introduction to Calculus
Explore the basics of Calculus.
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