Course Introduction and Table of Contents

Deep Learning Overview – Theory Session – Part 1

Deep Learning Overview – Theory Session – Part 2

Choosing Between ML or DL for the next AI project – Quick Theory Session

Preparing Your Computer – Part 1

Preparing Your Computer – Part 2

Python Basics – Assignment

Python Basics – Flow Control

Python Basics – Functions

Python Basics – Data Structures

Theano Library Installation and Sample Program to Test

TensorFlow library Installation and Sample Program to Test

Keras Installation and Switching Theano and TensorFlow Backends

Explaining Multi-Layer Perceptron Concepts

Explaining Neural Networks Steps and Terminology

First Neural Network with Keras – Understanding Pima Indian Diabetes Dataset

Explaining Training and Evaluation Concepts

Pima Indian Model – Steps Explained – Part 1

Pima Indian Model – Steps Explained – Part 2

Coding the Pima Indian Model – Part 1

Coding the Pima Indian Model – Part 2

Pima Indian Model – Performance Evaluation – Automatic Verification

Pima Indian Model – Performance Evaluation – Manual Verification

Pima Indian Model – Performance Evaluation – k-fold Validation – Keras

Pima Indian Model – Performance Evaluation – Hyper Parameters

Understanding Iris Flower Multi-Class Dataset

Developing the Iris Flower Multi-Class Model – Part 1

Developing the Iris Flower Multi-Class Model – Part 2

Developing the Iris Flower Multi-Class Model – Part 3

Understanding the Sonar Returns Dataset

Developing the Sonar Returns Model

Sonar Performance Improvement – Data Preparation – Standardization

Sonar Performance Improvement – Layer Tuning for Smaller Network

Sonar Performance Improvement – Layer Tuning for Larger Network

Understanding the Boston Housing Regression Dataset

Developing the Boston Housing Baseline Model

Boston Performance Improvement by Standardization

Boston Performance Improvement by Deeper Network Tuning

Boston Performance Improvement by Wider Network Tuning

Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 1

Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 2

Save and Load Model as YAML File – Pima Indian Dataset

Load and Predict using the Pima Indian Diabetes Model

Load and Predict using the Iris Flower Multi-Class Model

Load and Predict using the Sonar Returns Model

Load and Predict using the Boston Housing Regression Model

An Introduction to Checkpointing

Checkpoint Neural Network Model Improvements

Checkpoint Neural Network Best Model

Loading the Saved Checkpoint

Plotting Model Behavior History – Introduction

Plotting Model Behavior History – Coding

Dropout Regularization – Visible Layer – Part 1

Dropout Regularization – Visible Layer – Part 2

Dropout Regularization – Hidden Layer

Learning Rate Schedule using Ionosphere Dataset

Time Based Learning Rate Schedule – Part 1

Time Based Learning Rate Schedule – Part 2

Drop Based Learning Rate Schedule – Part 1

Drop Based Learning Rate Schedule – Part 2

Convolutional Neural Networks – Part 1

Convolutional Neural Networks – Part 2

Introduction to MNIST Handwritten Digit Recognition Dataset

Downloading and Testing MNIST Handwritten Digit Recognition Dataset

MNIST Multi-Layer Perceptron Model Development – Part 1

MNIST Multi-Layer Perceptron Model Development – Part 2

Convolutional Neural Network Model using MNIST – Part 1

Convolutional Neural Network Model using MNIST – Part 2

Large CNN using MNIST

Load and Predict using the MNIST CNN Model

Introduction to Image Augmentation using Keras

Augmentation using Sample Wise Standardization

Augmentation using Feature Wise Standardization & ZCA Whitening

Augmentation using Rotation and Flipping

Saving Augmentation

CIFAR-10 Object Recognition Dataset – Understanding and Loading

Simple CNN using CIFAR-10 Dataset – Part 1

Simple CNN using CIFAR-10 Dataset – Part 2

Simple CNN using CIFAR-10 Dataset – Part 3

Train and Save CIFAR-10 Model

Load and Predict using CIFAR-10 CNN Model

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