Section 1 : Introduction

Lecture 1 Introduction 1:47

Section 2 : Artificial Intelligence Basics

Lecture 2 Why to learn artificial intelligence and machin 4:2
Lecture 3 Types of artificial intelligence learning metho 5:54

Section 3 : Hopfield Neural Network Theory

Lecture 4 Hopfield neural network introduction 3:26
Lecture 5 Hopfield network - weights
Lecture 6 Hopfield neural network - Hebbian learning 5:35
Lecture 7 Hopfield neural network - energy 7:21
Lecture 8 Measuring the energy of the network 4:54
Lecture 9 Hopfield neural network example 8:4

Section 4 : Hopfield Neural Network Implementation

Lecture 10 Hopfield network implementation - utils 6:20
Lecture 11 Hopfield network implementation - matrix opera 11:42
Lecture 12 Hopfield network implementation - network 9:44
Lecture 13 Hopfield network implementation - running the

Section 5 : Neural Networks With Backpropagation Theory

Lecture 14 Artificial neural networks - inspiration 5:20
Lecture 15 Artificial neural networks - layers
Lecture 16 Artificial neural networks - the model 5:10
Lecture 17 Why to use activation functions 6:47
Lecture 18 Neural networks - the big picture 9:7
Lecture 19 Using bias nodes in the neural network 1:44
Lecture 20 How to measure the error of the network
Lecture 21 Optimization with gradient descent 8:28
Lecture 22 Gradient descent with backpropagation 6:34
Lecture 23 Backpropagation explained 12:13
Lecture 24 Applications of neural networks I - character 5:9
Lecture 25 Applications of neural networks II - stock mar 4:46
Lecture 26 Deep learning 5:4
Lecture 27 Types of neural networks 3:50

Section 6 : Single Perceptron Model

Lecture 28 Perceptron model training 6:18
Lecture 29 Perceptron model implementation I 8:3
Lecture 30 Perceptron model implementation II 8:54
Lecture 31 Perceptron model implementation III 6:31
Lecture 32 Trying to solve XOR problem
Lecture 33 Conclusion linearity and hidden layers 2:34

Section 7 : Backpropagation Implementation

Lecture 34 Structure of the feedforward network 5:39
Lecture 35 Backpropagation implementation I - activation 4:45
Lecture 36 Backpropagation implementation II - NeuralNetw 8:25
Lecture 37 Backpropagation implementation III - Layer 5:32
Lecture 38 Backpropagation implementation IV - run 7:3
Lecture 39 Backpropagation implementation V - train 7:2

Section 8 : Logical Operators

Lecture 40 Logical operators introduction 2:6
Lecture 41 Running the neural network AND 8:0
Lecture 42 Running the neural network OR 3:17
Lecture 43 Running the neural network XOR 2:31

Section 9 : Clustering

Lecture 44 Clustering with neural networks I 2:9
Lecture 45 Clustering with neural networks II 4:47

Section 10 : Classification - Iris Dataset

Lecture 46 About the Iris dataset 2:47
Lecture 47 Constructing the neural network 2:40
Lecture 48 Testing the neural network 6:54
Lecture 49 Calculating the accuracy of the model 4:52

Section 11 : Optical Character Recognition (OCR)

Lecture 50 Optical character recognition theory 3:33
Lecture 51 Installing paint.net 2:35
Lecture 52 Transform an image into numerical data 4:18
Lecture 53 Creating the datasets 2:1
Lecture 54 OCR with neural network 5:54

Section 12 : Course Materials (DOWNLOADS)

Lecture 55 Course materials Text