Deep Learning & Neural Networks Python – Keras
Last updated: June 13, 2024
Language: English
Flexible Schedule
Imagine transforming your career with cutting-edge knowledge in Deep Learning and Neural Networks using Python and Keras. This comprehensive course delves into the intricate world of artificial intelligence, providing a robust foundation in both theory and practical application. You’ll explore essential concepts like multi-layer perceptrons, convolutional neural networks, and diverse datasets including Pima Indian Diabetes, Iris Flower, and MNIST Handwritten Digit Recognition.
Through a structured curriculum, the course guides you from the basics of Python to advanced neural network models. You will learn to install and use libraries like Theano and TensorFlow, develop and evaluate models, and implement performance improvements. Techniques such as dropout regularisation and learning rate schedules will enhance your skills, ensuring you can handle real-world AI challenges effectively.
By the end of the course, you’ll be equipped to load, predict, and improve models across various datasets. With practical examples and step-by-step instructions, you will gain the expertise to save and load models, apply image augmentation, and use checkpointing for neural network improvements. This course is an invaluable resource for anyone looking to excel in the AI and machine learning industry.
Learning Outcomes
- Master the fundamentals and advanced concepts of deep learning and neural networks using Python and Keras.
- Develop proficiency in using Theano and TensorFlow libraries for model development and evaluation.
- Gain expertise in handling and improving model performance using dropout regularisation and learning rate schedules.
- Acquire skills to implement and evaluate models on various datasets including Pima Indian Diabetes, Iris Flower, and MNIST.
- Learn to save, load, and predict using trained models with practical hands-on examples and step-by-step guidance.
Why buy this Deep Learning & Neural Networks Python – Keras?
- Unlimited access to the course for a lifetime.
- Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course.
- Structured lesson planning in line with industry standards.
- Immerse yourself in innovative and captivating course materials and activities.
- Assessments designed to evaluate advanced cognitive abilities and skill proficiency.
- Flexibility to complete the Course at your own pace, on your own schedule.
- Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.
- Unlock career resources for CV improvement, interview readiness, and job success.
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?
- Aspiring data scientists seeking to specialise in deep learning.
- AI enthusiasts wanting to enhance their knowledge in neural networks.
- Software developers aiming to integrate AI capabilities into their projects.
- Students pursuing a career in artificial intelligence and machine learning.
- Professionals transitioning into the AI and data science field.
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
- Data Scientist: £50,000 – £80,000 per year
- Machine Learning Engineer: £45,000 – £75,000 per year
- AI Research Scientist: £55,000 – £85,000 per year
- Software Developer: £35,000 – £60,000 per year
- Data Analyst: £30,000 – £50,000 per year
- Deep Learning Specialist: £60,000 – £90,000 per year
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 | ||
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This course includes:
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Skill Level
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Duration
11 hours, 11 minutes -
Students
4 Students