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15
Course overview Learn about Deep Learning & Neural Networks Python – Keras quickly and enhance your skills. You will understand …
Learn about Deep Learning & Neural Networks Python – Keras quickly and enhance your skills. You will understand Deep Learning & Neural Networks Python – Keras and its key areas, covering the fundamentals and beyond. The Deep Learning & Neural Networks Python – Keras course is divided into small modules that are easy to grasp even by complete beginners. Plus, the Deep Learning & Neural Networks Python – Keras course comes with tutor support, so if you have any questions related to Deep Learning & Neural Networks Python – Keras, you can ask them via live chat or email.
Completing this Deep Learning & Neural Networks Python – Keras course will give you the confidence to stand out to prospective employers in the Deep Learning & Neural Networks Python – Keras industry & other relevant sectors, climb up the career ladder and boost your earning potential in no time.
Enrol in the Deep Learning & Neural Networks Python – Keras course today and invest in your bright future.
Anyone with an interest in Deep Learning & Neural Networks Python – Keras will find this course valuable. Whether you are a student/ aspiring professional aiming to explore your opportunities in Deep Learning & Neural Networks Python – Keras sectors, then this Deep Learning & Neural Networks Python – Keras course might just be the perfect opportunity for you.
After successfully completing the Deep Learning & Neural Networks Python – Keras course, you can apply for a CPD accredited course in both digital & physical format.
The pricing for the Deep Learning & Neural Networks Python – Keras course completion certificate is as follows:
Complete your Deep Learning & Neural Networks Python – Keras course successfully and develop yourself to enhance your chances of succeeding in Deep Learning & Neural Networks Python – Keras sectors and other relevant fields.
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 | 00:06:00 | ||
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 | 00:09:00 | ||
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 and Load Model as YAML File - Pima Indian Dataset | |||
Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 2 | 00:08:00 | ||
Load and Predict using the Pima Indian Diabetes Model | |||
Save and Load Model as YAML File – Pima Indian Dataset | 00:05:00 | ||
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 | 00:00:00 | ||
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 | |||
RECOMENDED READINGS | |||
Recomended Readings | 00:00:00 |
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