Deep Learning & Neural Networks Python - Keras

Course Overview Learn how to make a genuine difference in your life by taking our popular Deep Learning & Neural …

Deep Learning & Neural Networks Python - Keras

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Intermediate
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11 hours, 7 minutes

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1 year
Intermediate
Number of Units83
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11 hours, 7 minutes

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Curriculum

Course Overview

Learn how to make a genuine difference in your life by taking our popular Deep Learning & Neural Networks Python – Keras. Our commitment to online learning and our technical experience have been put to excellent use within the content of these educational modules. By enrolling today, you can take your knowledge of Deep Learning & Neural Networks Python – Kerasto a whole new level and quickly reap the rewards of your study in the field you have chosen.

We are confident that you will find the skills and information that you will need to succeed in this area and excel in the eyes of others. Do not rely on substandard training or half-hearted education. Commit to the best, and we will help you reach your full potential whenever and wherever you need us.

Please note that Deep Learning & Neural Networks Python – Keras provides valuable and significant theoretical training for all. However, it does not offer official qualifications for professional practice. Always check details with the appropriate authorities or management.

Learning Outcomes

Instant access to verified and reliable information
Participation in inventive and interactive training exercises
Quick assessment and guidance for all subjects
CPD accreditation for proof of acquired skills and knowledge
Freedom to study in any location and at a pace that suits you
Expert support from dedicated tutors committed to online learning

Your Path to Success

By completing the training in Deep Learning & Neural Networks Python – Keras, you will be able to significantly demonstrate your acquired abilities and knowledge of Deep Learning & Neural Networks Python – Keras. This can give you an advantage in career progression, job applications, and personal mastery in this area.

Is This Course Right for You?

This course is designed to provide an introduction to Deep Learning & Neural Networks Python – Keras and offers an excellent way to gain the vital skills and confidence to start a successful career. It also provides access to proven educational knowledge about the subject and will support those wanting to attain personal goals in this area. Full-time and part-time learners are equally supported, and the study periods are entirely customisable to your needs.

Assessment Process

Once you have completed all the modules in the Deep Learning & Neural Networks Python – Keras course, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.

Show off Your New Skills with a Certification of Completion

The learners have to successfully complete the assessment of this Deep Learning & Neural Networks Python – Keras course to achieve the CPD accredited certificate. Digital certificates can be ordered for only £10. Learners can purchase printed hard copies inside the UK for £29, and international students can purchase printed hard copies for £39.

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	00:10:00
	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	00:07:00
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	00:12:00
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	00:12:00
RECOMENDED READINGS
	Recomended Readings	00:00:00
Claim Your Certificate
	Claim Your Certificate	00:00:00

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Once you've confirmed the details, proceed to complete the payment using your preferred payment method (credit/debit card, PayPal, etc.).

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Certificate

After completing all the required modules or assessments within the course, you may be eligible to receive a certificate of completion.

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Deep Learning & Neural Networks Python - Keras

Deep Learning & Neural Networks Python - Keras

Course Curriculum

How do our courses work?

Purchase and payment

Once you've confirmed the details, proceed to complete the payment using your preferred payment method (credit/debit card, PayPal, etc.).

Course access

Upon completing the purchase, you'll receive an email containing instructions on how to access the course.

Certificate

After completing all the required modules or assessments within the course, you may be eligible to receive a certificate of completion.

Course Assistance

If you encounter any difficulties or have questions while taking the course, you can typically reach out to the course instructor or support team for assistance.

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Deep Learning & Neural Networks Python - Keras

Deep Learning & Neural Networks Python - Keras

Course Curriculum

How do our courses work?

Purchase and payment

Once you've confirmed the details, proceed to complete the payment using your preferred payment method (credit/debit card, PayPal, etc.).

Course access

Upon completing the purchase, you'll receive an email containing instructions on how to access the course.

Certificate

After completing all the required modules or assessments within the course, you may be eligible to receive a certificate of completion.

Course Assistance

If you encounter any difficulties or have questions while taking the course, you can typically reach out to the course instructor or support team for assistance.

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