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Deep Learning & Neural Networks Python - Keras

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Course overview Learn about Deep Learning & Neural Networks Python – Keras quickly and enhance your skills. You will understand …

Course overview

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.

What's inside

Who is this course for?

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.

Certification

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:

Learning outcomes

Explore a diverse range of aspects of this field after the successful completion of the Deep Learning & Neural Networks Python - Keras course
Get inside-out knowledge on Deep Learning & Neural Networks Python - Keras topic from the industry experts
Acquire skills that will help you to explore many opportunities

Career path

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 Curriculum

80 Modules 10 hours, 40 minutes

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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