Section 1 : Course Overview, Installs, and Setup

Lecture 1 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf
Lecture 2 Installation and Environment Setup 18:16

Section 2 : Crash Course NumPy

Lecture 3 Introduction to NumPy
Lecture 4 NumPy Arrays 10:39
Lecture 5 NumPy Arrays Part Two 8:4
Lecture 6 Numpy Index Selection 11:30
Lecture 7 NumPy Operations 6:39
Lecture 8 Numpy Exercises 1:7
Lecture 9 Numpy Exercises - Solutions 6:57

Section 3 : Crash Course Pandas

Lecture 10 Pandas Overview 1:4
Lecture 11 Pandas Series 9:56
Lecture 12 Pandas DataFrames - Part One 13:18
Lecture 13 Pandas DataFrames - Part Two 11:4
Lecture 14 GroupBy Operations 5:36
Lecture 15 Pandas Operations
Lecture 16 Data Input and Output 10:11
Lecture 17 Pandas Exercises 3:32
Lecture 18 Pandas Exercises - Solutions 8:29

Section 4 : PyTorch Basics

Lecture 19 PyTorch Basics Introduction 3:15
Lecture 20 Tensor Basics 8:4
Lecture 21 Tensor Basics - Part Two 15:7
Lecture 22 Tensor Operations 13:24
Lecture 23 Tensor Operations - Part Two 6:21
Lecture 24 PyTorch Basics - Exercise 2:27
Lecture 25 PyTorch Basics - Exercise Solutions 5:16

Section 5 : Machine Learning Concepts Overview

Lecture 26 What is Machine Learning 3:33
Lecture 27 Supervised Learning 8:16
Lecture 28 Overfitting 7:52
Lecture 29 Evaluating Performance - Classification Error Metrics 16:37
Lecture 30 Evaluating Performance - Regression Error Metrics 5:31
Lecture 31 Unsupervised Learning 4:39

Section 6 : ANN - Artificial Neural Networks

Lecture 32 Introduction to ANN Section 1:39
Lecture 33 Theory - Perceptron Model 10:34
Lecture 34 Theory - Neural Network
Lecture 35 Theory - Activation Functions 10:34
Lecture 36 Multi-Class Classification 10:29
Lecture 37 Theory - Cost Functions and Gradient Descent 18:8
Lecture 38 Theory - BackPropagation 14:42
Lecture 39 PyTorch Gradients 12:17
Lecture 40 Linear Regression with PyTorch 10:56
Lecture 41 Linear Regression with PyTorch - Part Two 20:25
Lecture 42 DataSets with PyTorch 15:53
Lecture 43 Basic Pytorch ANN - Part One 11:28
Lecture 44 Basic PyTorch ANN - Part Two 15:28
Lecture 45 Basic PyTorch ANN - Part Three 14:17
Lecture 46 Introduction to Full ANN with PyTorch 6:47
Lecture 47 Full ANN Code Along - Regression - Part One - Feature Engineering 19:29
Lecture 48 Full ANN Code Along - Regression - Part 2 - Categorical and Continuous Features 19:37
Lecture 49 Full ANN Code Along - Regression - Part Three - Tabular Model 17:3
Lecture 50 Full ANN Code Along - Regression - Part Four - Training and Evaluation 16:36
Lecture 51 Full ANN Code Along - Classification Example 6:46
Lecture 52 ANN - Exercise Overview 5:24
Lecture 53 ANN - Exercise Solutions 16:19

Section 7 : CNN - Convolutional Neural Networks

Lecture 54 Introduction to CNNs
Lecture 55 Understanding the MNIST data set 3:19
Lecture 56 ANN with MNIST - Part One - Data 19:16
Lecture 57 ANN with MNIST - Part Two - Creating the Network 10:28
Lecture 58 ANN with MNIST - Part Three - Training 15:22
Lecture 59 ANN with MNIST - Part Four - Evaluation 9:9
Lecture 60 Image Filters and Kernels 11:30
Lecture 61 Convolutional Layers 13:55
Lecture 62 Pooling Layers 6:42
Lecture 63 MNIST Data Revisited 2:6
Lecture 64 MNIST with CNN - Code Along - Part One 18:16
Lecture 65 MNIST with CNN - Code Along - Part Two 18:13
Lecture 66 About Certification Pdf
Lecture 67 CIFAR-10 DataSet with CNN - Code Along - Part One 7:8
Lecture 68 CIFAR-10 DataSet with CNN - Code Along - Part Two 18:35
Lecture 69 Loading Real Image Data - Part One 16:7
Lecture 70 Loading Real Image Data - Part Two 18:21
Lecture 71 CNN on Custom Images - Part One - Loading Data 22:14
Lecture 72 About Proctor Testing Pdf
Lecture 73 CNN on Custom Images - Part Three - PreTrained Networks 14:8
Lecture 74 CNN Exercise 2:44
Lecture 75 CNN Exercise Solutions 7:47

Section 8 : Recurrent Neural Networks

Lecture 76 Introduction to Recurrent Neural Networks 1:55
Lecture 77 RNN Basic Theory 7:36
Lecture 78 Vanishing Gradients 6:42
Lecture 79 LSTMS and GRU 11:16
Lecture 80 RNN Batches Theory 7:44
Lecture 81 RNN - Creating Batches with Data 12:4
Lecture 82 Basic RNN - Creating the LSTM Model 12:50
Lecture 83 Basic RNN - Training and Forecasting
Lecture 84 RNN on a Time Series - Part One 14:30
Lecture 85 RNN on a Time Series - Part Two 18:39
Lecture 86 RNN Exercise 4:9
Lecture 87 RNN Exercise - Solutions 11:26

Section 9 : Using a GPU with PyTorch and CUDA

Lecture 88 Why do we need GPUs 13:1
Lecture 89 Using GPU for PyTorch 17:34

Section 10 : NLP with PyTorch

Lecture 90 Introduction to NLP with PyTorch 2:31
Lecture 91 Encoding Text Data 15:44
Lecture 92 Generating Training Batches 14:34
Lecture 93 Creating the LSTM Model 12:28
Lecture 94 Training the LSTM Model 11:49
Lecture 95 OUR MODEL FOR DOWNLOAD Text
Lecture 96 Generating Predictions 10:26