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Deep Learning & Neural Networks Python - Keras
By Studyhub UK
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Highlights
On-Demand course
11 hours 11 minutes
All levels
Description
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 | ||
About The Provider
Studyhub UK
London, England
(3)Studyhub is a premier online learning platform which aims to help individuals worldwide to realise their educational dreams. For 5 years, we have been dedicated...
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