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