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What you’ll learn
Basics of neural networks
Hopfield networks
Concrete implementation of neural networks
Backpropagation
Optical character recognition

“Introduction

“Neural Networks Introduction
Axons and neurons in the human brain
Modeling human brain
Learning paradigms
Artificial neurons – the model
Artificial neurons – activations functions
ARTICLE: activation functions
Artificial neurons – an example
Neural networks – the big picture
Applications of neural networks

“Hopfield Neural Network
Hopfield neural network introduction
Hopfield network energy
Hopfield neural network training and learning
Hopfield neural network problems
Hopfield neural network example

“Neural Networks With Backpropagation Theory
Feedforward neural networks
Optimization – cost function
ARTICLE: optimization algorithms
Simplified feedforward network
Feedforward neural network topology
The learning algorithm
Error calculation
Backpropagation
ARTICLE: derivation of backpropagation
Resilient propagation
Deep learning

“Types of Neural Networks

“Single Perceptron Model
Perceptron model training
Trying to solve XOR problem
Conclusion: linearity and hidden layers

“Backpropagation Implementation
Structure of the feedforward network

“Logical Operators
Logical operators introduction
Running the neural network: AND
Running the neural network: OR
Running the neural network: XOR

“Clustering

“Classification – Iris Dataset
About the Iris dataset
Constructing the neural network
Testing the neural network

Optical Character Recognition (OCR)
Optical character recognition theory
Installing paint.net
Transform an image into numerical data
Creating the datasets
OCR with neural network

Course Materials (DOWNLOADS)
Course materials

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