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- Original price was: £175.00.£21.00Current price is: £21.00.
- 1 year
- Diploma
- Course Certificate
- Number of Units83
- Number of Quizzes0
- 11 hours, 7 minutes Gift this course
Course Overview
In this competitive job market, you need to have some specific skills and knowledge to start your career and establish your position. This Deep Learning & Neural Networks Python – Keras will help you understand the current demands, trends and skills in the sector. The course will provide you with the essential skills you need to boost your career growth in no time.
The Deep Learning & Neural Networks Python – Keras will give you clear insight and understanding about your roles and responsibilities, job perspective and future opportunities in this field. You will be familiarised with various actionable techniques, career mindset, regulations and how to work efficiently.
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 work toward 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.
Learning Objectives
- Learn the fundamental skills you require to be an expert
- Explore different techniques used by professionals
- Find out the relevant job skills & knowledge to excel in this profession
- Get a clear understanding of the job market and current demand
- Update your skills and fill any knowledge gap to compete in the relevant industry
- CPD accreditation for proof of acquired skills and knowledge
Who is this Course for?
Whether you are a beginner or an existing practitioner, our CPD accredited Deep Learning & Neural Networks Python – Keras is perfect for you to gain extensive knowledge about different aspects of the relevant industry to hone your skill further.
It is also great for working professionals who have acquired practical experience but require theoretical knowledge with a credential to support their skill, as we offer CPD accredited certification to boost up your resume and promotion prospects.
Entry Requirement
Anyone interested in learning more about this subject should take this Deep Learning & Neural Networks Python – Keras. This course will help you grasp the basic concepts as well as develop a thorough understanding of the subject.
The course is open to students from any academic background, as there is no prerequisites to enrol on this course. The course materials are accessible from an internet enabled device at anytime of the day.
CPD Certificate from Course Gate
At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £9 and the hard copy for £15. Also, you can order both PDF and hardcopy certificates for £22.
Career path
The Deep Learning & Neural Networks Python – Keras will help you to enhance your knowledge and skill in this sector. After accomplishing this course, you will enrich and improve yourself and brighten up your career in the relevant job market.
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 Te | |||
| 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 | 00:08:00 | ||
| MNIST Handwritten Digit Recognition Dataset | |||
| Introduction to MNIST Handwritten Digit Recognition Dataset | 00:06:00 | ||
| Introduction to MNIST Handwritten Digit Recognition Dataset | 00:06:00 | ||
| 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 | ||
| Certificate and Transcript | |||
| Order Your Certificates or Transcripts | 00:00:00 | ||
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- Original price was: £175.00.£21.00Current price is: £21.00.
- 1 year
- Diploma
- Course Certificate
- Number of Units83
- Number of Quizzes0
- 11 hours, 7 minutes Gift this course
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