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Course Introduction and Table of Contents | |||
Course Introduction and Table of Contents | 00:11:00 | ||
Deep Learning Overview | |||
Deep Learning Overview - Theory Session - Part 1 | 00:06:00 | ||
Deep Learning Overview - Theory Session - Part 2 | 00:07:00 | ||
Choosing Between ML or DL for the next AI project - Quick Theory Session | |||
Choosing Between ML or DL for the next AI project - Quick Theory Session | 00:09:00 | ||
Preparing Your Computer | |||
Preparing Your Computer - Part 1 | 00:07:00 | ||
Preparing Your Computer - Part 2 | 00:06:00 | ||
Python Basics | |||
Python Basics - Assignment | 00:09:00 | ||
Python Basics - Flow Control | 00:09:00 | ||
Python Basics - Functions | 00:04:00 | ||
Python Basics - Data Structures | 00:12:00 | ||
Theano Library Installation and Sample Program to Test | |||
Theano Library Installation and Sample Program to Test | 00:11:00 | ||
TensorFlow library Installation and Sample Program to Test | |||
TensorFlow library Installation and Sample Program to Test | 00:09:00 | ||
Keras Installation and Switching Theano and TensorFlow Backends | |||
Keras Installation and Switching Theano and TensorFlow Backends | 00:10:00 | ||
Explaining Multi-Layer Perceptron Concepts | |||
Explaining Multi-Layer Perceptron Concepts | 00:03:00 | ||
Explaining Neural Networks Steps and Terminology | |||
Explaining Neural Networks Steps and Terminology | 00:10:00 | ||
First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset | |||
First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset | 00:07:00 | ||
Explaining Training and Evaluation Concepts | |||
Explaining Training and Evaluation Concepts | 00:11:00 | ||
Pima Indian Model - Steps Explained | |||
Pima Indian Model - Steps Explained - Part 1 | 00:09:00 | ||
Pima Indian Model - Steps Explained - Part 2 | 00:07:00 | ||
Coding the Pima Indian Model | |||
Coding the Pima Indian Model - Part 1 | 00:11:00 | ||
Coding the Pima Indian Model - Part 2 | 00:09:00 | ||
Pima Indian Model - Performance Evaluation | |||
Pima Indian Model - Performance Evaluation - Automatic Verification | 00:06:00 | ||
Pima Indian Model - Performance Evaluation - Manual Verification | 00:08:00 | ||
Pima Indian Model - Performance Evaluation - k-fold Validation - Keras | |||
Pima Indian Model - Performance Evaluation - k-fold Validation - Keras | 00:10:00 | ||
Pima Indian Model - Performance Evaluation - Hyper Parameters | |||
Pima Indian Model - Performance Evaluation - Hyper Parameters | 00:12:00 | ||
Understanding Iris Flower Multi-Class Dataset | |||
Understanding Iris Flower Multi-Class Dataset | 00:08:00 | ||
Developing the Iris Flower Multi-Class Model | |||
Developing the Iris Flower Multi-Class Model - Part 1 | 00:09:00 | ||
Developing the Iris Flower Multi-Class Model - Part 2 | 00:06:00 | ||
Developing the Iris Flower Multi-Class Model - Part 3 | 00:09:00 | ||
Understanding the Sonar Returns Dataset | |||
Understanding the Sonar Returns Dataset | 00:07:00 | ||
Developing the Sonar Returns Model | |||
Developing the Sonar Returns Model | 00:10:00 | ||
Sonar Performance Improvement - Data Preparation - Standardization | |||
Sonar Performance Improvement - Data Preparation - Standardization | 00:15:00 | ||
Sonar Performance Improvement - Layer Tuning for Smaller Network | |||
Sonar Performance Improvement - Layer Tuning for Smaller Network | 00:07:00 | ||
Sonar Performance Improvement - Layer Tuning for Larger Network | |||
Sonar Performance Improvement - Layer Tuning for Larger Network | 00:06:00 | ||
Understanding the Boston Housing Regression Dataset | |||
Understanding the Boston Housing Regression Dataset | 00:07:00 | ||
Developing the Boston Housing Baseline Model | |||
Developing the Boston Housing Baseline Model | 00:08:00 | ||
Boston Performance Improvement by Standardization | |||
Boston Performance Improvement by Standardization | 00:07:00 | ||
Boston Performance Improvement by Deeper Network Tuning | |||
Boston Performance Improvement by Deeper Network Tuning | 00:05:00 | ||
Boston Performance Improvement by Wider Network Tuning | |||
Boston Performance Improvement by Wider Network Tuning | 00:04:00 | ||
Save & Load the Trained Model as JSON File (Pima Indian Dataset) | |||
Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 1 | 00:09:00 | ||
Save & Load the Trained Model as JSON File (Pima Indian Dataset) - Part 2 | 00:08:00 | ||
Save and Load Model as YAML File - Pima Indian Dataset | |||
Save and Load Model as YAML File - Pima Indian Dataset | 00:05:00 | ||
Load and Predict using the Pima Indian Diabetes Model | |||
Load and Predict using the Pima Indian Diabetes Model | 00:09:00 | ||
Load and Predict using the Iris Flower Multi-Class Model | |||
Load and Predict using the Iris Flower Multi-Class Model | 00:08:00 | ||
Load and Predict using the Sonar Returns Model | |||
Load and Predict using the Sonar Returns Model | 00:10:00 | ||
Load and Predict using the Boston Housing Regression Model | |||
Load and Predict using the Boston Housing Regression Model | 00:08:00 | ||
An Introduction to Checkpointing | |||
An Introduction to Checkpointing | 00:06:00 | ||
Checkpoint Neural Network Model Improvements | |||
Checkpoint Neural Network Model Improvements | 00:10:00 | ||
Checkpoint Neural Network Best Model | |||
Checkpoint Neural Network Best Model | 00:04:00 | ||
Loading the Saved Checkpoint | |||
Loading the Saved Checkpoint | 00:05:00 | ||
Plotting Model Behavior History | |||
Plotting Model Behavior History - Introduction | 00:06:00 | ||
Plotting Model Behavior History - Coding | 00:08:00 | ||
Dropout Regularization - Visible Layer | |||
Dropout Regularization - Visible Layer - Part 1 | 00:11:00 | ||
Dropout Regularization - Visible Layer - Part 2 | 00:06:00 | ||
Dropout Regularization - Hidden Layer | |||
Dropout Regularization - Hidden Layer | 00:06:00 | ||
Learning Rate Schedule using Ionosphere Dataset - Intro | |||
Learning Rate Schedule using Ionosphere Dataset | 00:06:00 | ||
Time Based Learning Rate Schedule | |||
Time Based Learning Rate Schedule - Part 1 | 00:07:00 | ||
Time Based Learning Rate Schedule - Part 2 | 00:12:00 | ||
Drop Based Learning Rate Schedule | |||
Drop Based Learning Rate Schedule - Part 1 | 00:07:00 | ||
Drop Based Learning Rate Schedule - Part 2 | 00:08:00 | ||
Convolutional Neural Networks - Introduction | |||
Convolutional Neural Networks - Part 1 | 00:11:00 | ||
Convolutional Neural Networks - Part 2 | 00:06:00 | ||
MNIST Handwritten Digit Recognition Dataset | |||
Introduction to MNIST Handwritten Digit Recognition Dataset | 00:06:00 | ||
Downloading and Testing MNIST Handwritten Digit Recognition Dataset | 00:10:00 | ||
MNIST Multi-Layer Perceptron Model Development | |||
MNIST Multi-Layer Perceptron Model Development - Part 1 | 00:11:00 | ||
MNIST Multi-Layer Perceptron Model Development - Part 2 | 00:06:00 | ||
Convolutional Neural Network Model using MNIST | |||
Convolutional Neural Network Model using MNIST - Part 1 | 00:13:00 | ||
Convolutional Neural Network Model using MNIST - Part 2 | 00:12:00 | ||
Large CNN using MNIST | |||
Large CNN using MNIST | 00:09:00 | ||
Load and Predict using the MNIST CNN Model | |||
Load and Predict using the MNIST CNN Model | 00:14:00 | ||
Introduction to Image Augmentation using Keras | |||
Introduction to Image Augmentation using Keras | 00:11:00 | ||
Augmentation using Sample Wise Standardization | |||
Augmentation using Sample Wise Standardization | 00:10:00 | ||
Augmentation using Feature Wise Standardization & ZCA Whitening | |||
Augmentation using Feature Wise Standardization & ZCA Whitening | 00:04:00 | ||
Augmentation using Rotation and Flipping | |||
Augmentation using Rotation and Flipping | 00:04:00 | ||
Saving Augmentation | |||
Saving Augmentation | 00:05:00 | ||
CIFAR-10 Object Recognition Dataset - Understanding and Loading | |||
CIFAR-10 Object Recognition Dataset - Understanding and Loading | 00:12:00 | ||
Simple CNN using CIFAR-10 Dataset | |||
Simple CNN using CIFAR-10 Dataset - Part 1 | 00:09:00 | ||
Simple CNN using CIFAR-10 Dataset - Part 2 | 00:06:00 | ||
Simple CNN using CIFAR-10 Dataset - Part 3 | 00:08:00 | ||
Train and Save CIFAR-10 Model | |||
Train and Save CIFAR-10 Model | 00:08:00 | ||
Load and Predict using CIFAR-10 CNN Model | |||
Load and Predict using CIFAR-10 CNN Model | 00:16:00 | ||
RECOMENDED READINGS | |||
Recomended Readings | 00:00:00 |