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Course Overview

Become a trained professional from the safety and comfort of your own home by taking Deep Learning & Neural Networks Python – Keras . Whatever your situation and requirements, One Education can supply you with professional teaching, gained from industry experts, and brought to you for a great price with a limited-time discount.

One Education has been proud to produce an extensive range of best-selling courses, and Deep Learning & Neural Networks Python – Keras is one of our best offerings. It is crafted specially to promote easy learning at any location with an online device. Each topic has been separated into digestible portions that can be memorised and understood in the minimum of time. 

Teaching and training are more than just a job for the staff at One Education; we take pride in employing those who share our vision for e-learning and its importance in today’s society. To prove this, all learning materials for each course are available for at least one year after the initial purchase.  

All of our tutors and IT help desk personnel are available to answer any questions regarding your training or any technical difficulties. 

By completing Deep Learning & Neural Networks Python – Keras, you will have automatically earnt an e-certificate that is industry-recognised and will be a great addition to your competencies on your CV.

Whatever your reason for studying Deep Learning & Neural Networks Python – Keras, make the most of this opportunity from One Education and excel in your chosen field.

Please be aware that there are no hidden fees, no sudden exam charges, and no other kind of unexpected payments. All costs will be made very clear before you even attempt to sign up. 

Course design

The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

You are taught through a combination of

  • Video lessons
  • Online study materials

Will I receive a certificate of completion?

Upon successful completion, you will qualify for the UK and internationally-recognised CPD accredited certification. You can choose to make your achievement formal by obtaining your PDF Certificate at the cost of £9 and Hard Copy Certificate for £15.

Why study this course

It doesn’t matter if you are an aspiring professional or absolute beginner; this course will enhance your expertise and boost your CV with critical skills and an accredited certification attesting to your knowledge.

The Deep Learning & Neural Networks Python – Keras is fully available to anyone, and no previous qualifications are needed to enrol. All One Education needs to know is that you are eager to learn and are over 16.

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 00:06:00
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 & 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

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