Overview
This comprehensive course on Deep Learning & Neural Networks Python – Keras will deepen your understanding on this topic.
After successful completion of this course you can acquire the required skills in this sector. This Deep Learning & Neural Networks Python – Keras comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market.
So enrol in this course today to fast-track your career ladder.
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.
Who is this course for?
There is no experience or previous qualifications required for enrolment on this Deep Learning & Neural Networks Python – Keras. It is available to all students, of all academic backgrounds.
Requirements
Our Deep Learning & Neural Networks Python – Keras is fully compatible with PC’s, Mac’s, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.
There is no time limit for completing this course, it can be studied in your own time at your own pace.
Career path
Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to—
- Open doors of opportunities
- Increase your adaptability
- Keep you relevant
- Boost confidence
And much more!
Course Curriculum
- 61 sections
- 122 lectures
- 00:00:00 total length
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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
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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
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Preparing Your Computer – Part 2 00:06:00
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Python Basics – Assignment 00:09:00
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Python Basics – Flow Control 00:10:00
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Python Basics – Functions 00:04:00
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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
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Pima Indian Model – Steps Explained – Part 2 00:07:00
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Coding the Pima Indian Model – Part 1 00:11:00
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Coding the Pima Indian Model – Part 2 00:09:00
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Pima Indian Model – Performance Evaluation – Automatic Verification 00:06:00
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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
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Developing the Iris Flower Multi-Class Model – Part 2 00:06:00
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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
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Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 2 00:08:00
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Dolph Lundgren Speech 00:06:00
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Load and Predict using the Pima Indian Diabetes Model 00:07:00
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David Schwimmer Interview 00:07:00
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Courteney Cox Interview 00:06:00
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Load and Predict using the Boston Housing Regression Model 00:08:00
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Reading Practice Example 00:11:00
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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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How to Improve Listening Skills 00:14:00
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George Foreman Interview 00:11:00
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How to Improve Vocabulary 00:20:00
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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
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Time Based Learning Rate Schedule – Part 2 00:12:00
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Drop Based Learning Rate Schedule – Part 1 00:07:00
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Drop Based Learning Rate Schedule – Part 2 00:08:00
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Convolutional Neural Networks – Part 1 00:11:00
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Convolutional Neural Networks – Part 2 00:06:00
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Introduction to MNIST Handwritten Digit Recognition Dataset 00:06:00
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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
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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
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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 00:12:00
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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
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Simple CNN using CIFAR-10 Dataset – Part 2 00:06:00
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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 00:12:00
Deep Learning & Neural Networks Python – Keras
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ALL COURSES FOR - £99 This course includes:
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Duration:11 hours, 28 minutes
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Access:1 year access
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Level:Intermediate
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CPD Points:10
