Section 1 : Introduction
Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 2 : Artificial Intelligence Basics
Lecture 1 Why to learn artificial intelligence and machine learning 00:04:11 Duration
Lecture 2 Introduction to machine learning 00:06:05 Duration

Section 3 : Installing Deep Learning Library
Lecture 1 Installing Java 00:04:12 Duration
Lecture 2 Installing Eclipse 00:03:58 Duration
Lecture 3 Installing Maven 00:03:23 Duration
Lecture 4 Cloning the libraries from Github 00:03:08 Duration

Section 4 : Multi-Layer Neural Networks (Deep Learning) Theory
Lecture 1 Deep neural networks 00:05:33 Duration
Lecture 2 Activation functions 00:09:54 Duration
Lecture 3 Loss functions 00:05:58 Duration
Lecture 4 Gradient descent stochastic gradient descent
Lecture 5 Hyperparameters 00:05:19 Duration

Section 5 : Deep Neural Networks Implementation
Lecture 1 Deep neural network implementation - XOR problem 00:11:19 Duration
Lecture 2 Deep neural network implementation - XOR problem II 00:05:34 Duration
Lecture 3 Deep neural network implementation - iris dataset 00:06:03 Duration
Lecture 4 Deep neural network implementation - iris dataset II 00:03:21 Duration
Lecture 5 ARTICLE Optimizers Explained (SGD, ADAGrad, ADAM

Section 6 : Convolutional Neural Networks Theory
Lecture 1 Convolutional neural networks basics 00:06:06 Duration
Lecture 2 Feature selection 00:04:17 Duration
Lecture 3 Convolutional neural networks - kernel 00:04:18 Duration
Lecture 4 Convolutional neural networks - kernel II 00:05:48 Duration
Lecture 5 Convolutional neural networks - pooling 00:05:51 Duration
Lecture 6 Convolutional neural networks - flattening 00:05:02 Duration
Lecture 7 Convolutional neural networks - illustration 00:02:39 Duration

Section 7 : Convolutional Neural Networks Implementation
Lecture 1 CNN implementation I - digit classification 00:05:20 Duration
Lecture 2 CNN implementation II - digit classification 00:07:17 Duration
Lecture 3 CNN implementation III - digit classification 00:06:45 Duration
Lecture 4 Emoji classification I - handling custom datasets 00:07:22 Duration
Lecture 5 Emoji classification II - the dataset 00:03:39 Duration
Lecture 6 Emoji classification III - convolutional network 00:03:24 Duration
Lecture 7 Emoji classification IV - test 00:03:58 Duration
Lecture 8 ARTICLE Regularization (L1, L2 and dropout)

Section 8 : Recurrent Neural Networks Theory
Lecture 1 Why do recurrent neural networks are important 00:04:34 Duration
Lecture 2 Recurrent neural networks basics
Lecture 3 Vanishing and exploding gradients problem
Lecture 4 Long-short term memory (LTSM) model
Lecture 5 Gated recurrent units (GRUs) 00:03:27 Duration

Section 9 : Recurrent Neural Networks Implementation
Lecture 1 Google's approach word2vec method 00:07:21 Duration
Lecture 2 Skip-Gram model fundamentals 00:11:53 Duration
Lecture 3 Text classification implementation - similar words 00:09:10 Duration
Lecture 4 Sentiment analysis implementation I 00:06:11 Duration
Lecture 5 Sentiment analysis implementation II 00:07:01 Duration
Lecture 6 Sentiment analysis implementation III 00:04:55 Duration
Lecture 7 Sentiment analysis implementation IV

Section 10 : Course Materials (DOWNLOADS)
Lecture 1 Course materials