Section 1 : Introduction

Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

Section 2 : Artificial Intelligence Basics

Lecture 2 Why to learn artificial intelligence and machine learning 4:11
Lecture 3 Introduction to machine learning 6:5

Section 3 : Installing Deep Learning Library

Lecture 4 Installing Java 4:12
Lecture 5 Installing Eclipse 3:58
Lecture 6 Installing Maven 3:23
Lecture 7 Cloning the libraries from Github 3:8

Section 4 : Multi-Layer Neural Networks (Deep Learning) Theory

Lecture 8 Deep neural networks 5:33
Lecture 9 Activation functions 9:54
Lecture 10 Loss functions 5:58
Lecture 11 Gradient descent stochastic gradient descent
Lecture 12 Hyperparameters 5:19

Section 5 : Deep Neural Networks Implementation

Lecture 13 Deep neural network implementation - XOR problem 11:19
Lecture 14 Deep neural network implementation - XOR problem II 5:34
Lecture 15 Deep neural network implementation - iris dataset 6:3
Lecture 16 Deep neural network implementation - iris dataset II 3:21
Lecture 17 ARTICLE Optimizers Explained (SGD, ADAGrad, ADAM Text

Section 6 : Convolutional Neural Networks Theory

Lecture 18 Convolutional neural networks basics 6:6
Lecture 19 Feature selection 4:17
Lecture 20 Convolutional neural networks - kernel 4:18
Lecture 21 Convolutional neural networks - kernel II 5:48
Lecture 22 Convolutional neural networks - pooling 5:51
Lecture 23 Convolutional neural networks - flattening 5:2
Lecture 24 Convolutional neural networks - illustration 2:39

Section 7 : Convolutional Neural Networks Implementation

Lecture 25 CNN implementation I - digit classification 5:20
Lecture 26 CNN implementation II - digit classification 7:17
Lecture 27 CNN implementation III - digit classification 6:45
Lecture 28 Emoji classification I - handling custom datasets 7:22
Lecture 29 Emoji classification II - the dataset 3:39
Lecture 30 Emoji classification III - convolutional network 3:24
Lecture 31 Emoji classification IV - test 3:58
Lecture 32 ARTICLE Regularization (L1, L2 and dropout) Text

Section 8 : Recurrent Neural Networks Theory

Lecture 33 Why do recurrent neural networks are important 4:34
Lecture 34 Recurrent neural networks basics
Lecture 35 Vanishing and exploding gradients problem
Lecture 36 Long-short term memory (LTSM) model
Lecture 37 Gated recurrent units (GRUs) 3:27

Section 9 : Recurrent Neural Networks Implementation

Lecture 38 Google's approach word2vec method 7:21
Lecture 39 Skip-Gram model fundamentals 11:53
Lecture 40 Text classification implementation - similar words 9:10
Lecture 41 Sentiment analysis implementation I 6:11
Lecture 42 Sentiment analysis implementation II 7:1
Lecture 43 Sentiment analysis implementation III 4:55
Lecture 44 Sentiment analysis implementation IV

Section 10 : Course Materials (DOWNLOADS)

Lecture 45 Course materials Text