Deep Learning & Neural Networks Python - Keras | Online Course & Certification

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Deep Learning & Neural Networks Python - Keras

Deep Learning & Neural Networks Python - Keras

Level: Intermediate
Modules: 82 Modules
Duration: 11 hours, 11 minutes
Students: 0

The course “Deep Learning & Neural Networks Python – Keras” provides a comprehensive introduction to deep learning using the Keras library in Python. It covers topics ranging from basic neural networks to more advanced concepts, such as convolutional neural networks, image augmentation, and performance improvement techniques for various datasets.

Learning Outcomes:

Understand the fundamental concepts of deep learning and how it differs from traditional machine learning.
Gain proficiency in using Keras, a powerful deep learning library, for building and training neural network models.
Develop practical skills in creating and optimizing neural network models for different datasets, including image recognition tasks and regression problems.

Why buy this Deep Learning & Neural Networks Python – Keras?

Unlimited access to the course for forever
Digital Certificate, Transcript, student ID all included in the price
Absolutely no hidden fees
Directly receive CPD accredited qualifications after course completion
Receive one to one assistance on every weekday from professionals
Immediately receive the PDF certificate after passing
Receive the original copies of your certificate and transcript on the next working day
Easily learn the skills and knowledge from the comfort of your home

Certification

After studying the course materials of the Deep Learning & Neural Networks Python – Keras there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60.

Who is this course for?

This Deep Learning & Neural Networks Python – Keras course is ideal for

Students
Recent graduates
Job Seekers
Anyone interested in this topic
People already working in the relevant fields and want to polish their knowledge and skill.

Prerequisites

This Deep Learning & Neural Networks Python – Keras does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Deep Learning & Neural Networks Python – Keras was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.

Career path

As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Deep Learning & Neural Networks Python – Keras is a great way for you to gain multiple skills from the comfort of your home.

Course Curriculum

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