#### 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