Development
QLS
Business
Healthcare
Health & Fitness
Technology
Teaching
Lifestyle
Design
- Overview
Educational accomplishments and income are closely correlated. Education and skill with endorsed certificates from credible and renowned authorities typically lead to better jobs with attractive salaries. Educated and skilled workers also have lower rates of unemployment. Therefore, skills and endorsed certificates to showcase are essential for people early in their careers.
- Why Choose Imperial Academy?
Imperial Academy offers this premium Deep Learning & Neural Networks Python – Keras course to ensure security in your career. In addition, this comprehensive Deep Learning & Neural Networks Python – Keras course will assist you in building relevant skills that will help you find a job in the related sectors. Also, the Certificate you’ll get after completing the Deep Learning & Neural Networks Python – Keras will put your head and shoulder above others in front of potential employers.
Become the person who would attract the results you seek. What you plant now, you will harvest later. So, grab this opportunity and start learning Deep Learning & Neural Networks Python – Keras!
- What Imperial Academy Offers You
- QLS/ CPD/ CIQ Accredited
- 24/7 Assistance from our Support Team
- 100% Online
- Self-paced course
- Bite-sized Audio-visual Modules
- Rich Learning Materials
- Developed by Industry Specialists
- Career Guidance
- Course Design
Learn at your own pace from the comfort of your home, as the rich learning materials of this premium course is accessible from any place at any time. The advanced course curriculums are divided into tiny bite-sized audio-visual modules by industry specialists with years of experience behind them.
- Audio-visual Lessons
- Online Study Materials
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 | ||
Certificate of Achievement
Learners will get an certificate of achievement directly at their doorstep after successfully completing the course!
It should also be noted that international students must pay £10 for shipping cost.
CPD Accredited Certification
Upon successfully completing the course, you will be qualified for CPD Accredited Certificate. Certification is available –
- PDF Certificate £7.99
- Hard Copy Certificate £14.99
Related Courses
Recent Courses
