Section 1 : Introduction
Lecture 1 Introduction 00:01:47 Duration

Section 2 : Artificial Intelligence Basics
Lecture 1 Why to learn artificial intelligence and machin 00:04:02 Duration
Lecture 2 Types of artificial intelligence learning metho 00:05:54 Duration

Section 3 : Hopfield Neural Network Theory
Lecture 1 Hopfield neural network introduction 00:03:26 Duration
Lecture 2 Hopfield network - weights
Lecture 3 Hopfield neural network - Hebbian learning 00:05:35 Duration
Lecture 4 Hopfield neural network - energy 00:07:21 Duration
Lecture 5 Measuring the energy of the network 00:04:54 Duration
Lecture 6 Hopfield neural network example 00:08:04 Duration

Section 4 : Hopfield Neural Network Implementation
Lecture 1 Hopfield network implementation - utils 00:06:20 Duration
Lecture 2 Hopfield network implementation - matrix opera 00:11:42 Duration
Lecture 3 Hopfield network implementation - network 00:09:44 Duration
Lecture 4 Hopfield network implementation - running the

Section 5 : Neural Networks With Backpropagation Theory
Lecture 1 Artificial neural networks - inspiration 00:05:20 Duration
Lecture 2 Artificial neural networks - layers
Lecture 3 Artificial neural networks - the model 00:05:10 Duration
Lecture 4 Why to use activation functions 00:06:47 Duration
Lecture 5 Neural networks - the big picture 00:09:07 Duration
Lecture 6 Using bias nodes in the neural network 00:01:44 Duration
Lecture 7 How to measure the error of the network
Lecture 8 Optimization with gradient descent 00:08:28 Duration
Lecture 9 Gradient descent with backpropagation 00:06:34 Duration
Lecture 10 Backpropagation explained 00:12:13 Duration
Lecture 11 Applications of neural networks I - character 00:05:09 Duration
Lecture 12 Applications of neural networks II - stock mar 00:04:46 Duration
Lecture 13 Deep learning 00:05:04 Duration
Lecture 14 Types of neural networks 00:03:50 Duration

Section 6 : Single Perceptron Model
Lecture 1 Perceptron model training 00:06:18 Duration
Lecture 2 Perceptron model implementation I 00:08:03 Duration
Lecture 3 Perceptron model implementation II 00:08:54 Duration
Lecture 4 Perceptron model implementation III 00:06:31 Duration
Lecture 5 Trying to solve XOR problem
Lecture 6 Conclusion linearity and hidden layers 00:02:34 Duration

Section 7 : Backpropagation Implementation
Lecture 1 Structure of the feedforward network 00:05:39 Duration
Lecture 2 Backpropagation implementation I - activation 00:04:45 Duration
Lecture 3 Backpropagation implementation II - NeuralNetw 00:08:25 Duration
Lecture 4 Backpropagation implementation III - Layer 00:05:32 Duration
Lecture 5 Backpropagation implementation IV - run 00:07:03 Duration
Lecture 6 Backpropagation implementation V - train 00:07:02 Duration

Section 8 : Logical Operators
Lecture 1 Logical operators introduction 00:02:06 Duration
Lecture 2 Running the neural network AND 00:08:00 Duration
Lecture 3 Running the neural network OR 00:03:17 Duration
Lecture 4 Running the neural network XOR 00:02:31 Duration

Section 9 : Clustering
Lecture 1 Clustering with neural networks I 00:02:09 Duration
Lecture 2 Clustering with neural networks II 00:04:47 Duration

Section 10 : Classification - Iris Dataset
Lecture 1 About the Iris dataset 00:02:47 Duration
Lecture 2 Constructing the neural network 00:02:40 Duration
Lecture 3 Testing the neural network 00:06:54 Duration
Lecture 4 Calculating the accuracy of the model 00:04:52 Duration

Section 11 : Optical Character Recognition (OCR)
Lecture 1 Optical character recognition theory 00:03:33 Duration
Lecture 2 Installing paint.net 00:02:35 Duration
Lecture 3 Transform an image into numerical data 00:04:18 Duration
Lecture 4 Creating the datasets 00:02:01 Duration
Lecture 5 OCR with neural network 00:05:54 Duration

Section 12 : Course Materials (DOWNLOADS)
Lecture 1 Course materials