About this course

This course provides a comprehensive introduction to deep learning, covering key concepts, algorithms, and hands-on implementation using the Keras library. Through engaging lectures, practical labs, and quizzes, you’ll develop a solid foundation in deep learning techniques and gain experience building real-world models.

Begin your deep learning journey with an introduction to the fundamental concepts of artificial intelligence, neural networks, and deep learning. Learn how artificial neurons mimic the human brain and explore the structure of neural networks. Through hands-on labs, you’ll gain practical experience in building artificial neural networks, reinforcing key principles before testing your knowledge with quizzes and assessments.

Dive deeper into the mechanics of deep learning by understanding key optimization techniques such as gradient descent and backpropagation. Discover the challenges of training deep networks, including the vanishing gradient problem, and explore the role of activation functions in improving model performance. Practical labs provide hands-on experience in implementing these techniques, ensuring you grasp their real-world applications.

Explore the power of Keras and other deep learning libraries used in AI development. Learn how to build regression and classification models using Keras, and apply your knowledge in lab exercises focused on implementing these models. This module equips you with essential tools for developing deep learning applications efficiently.

Expand your deep learning expertise by exploring different model architectures. Compare shallow and deep neural networks, and gain insights into specialized models like convolutional neural networks (CNNs) for image processing, recurrent neural networks (RNNs) for sequence data, and transformers for advanced AI applications. Hands-on labs guide you through implementing CNNs and transformers in Keras, reinforcing theoretical concepts with practical applications.

Put your knowledge to the test with a final project that challenges you to build a deep learning model for classification and captioning tasks. Apply the skills you’ve gained throughout the course to develop and evaluate your model, demonstrating your ability to create real-world AI solutions. Upon submission and evaluation, you’ll have a tangible project to showcase your deep learning expertise.

This course is designed for professionals and students looking to enhance their deep learning skills. Whether you’re aiming for a career in AI, data science, or software development, this hands-on learning experience will provide the foundation you need.

Learning Objectives:

After completing this course, you will be able to:

Define key concepts such as neural networks, deep learning models, and activation functions.
Explain the differences between supervised and unsupervised deep learning models.
Implement deep learning models using Keras to solve real-world problems.
Compare and contrast different deep learning architectures, including convolutional and recurrent neural networks.
Assess the performance of deep learning models using appropriate metrics.
Design, build, train, and test deep learning models using the Keras library.

Course Syllabus:

Welcome

Video: Course Introduction
General Information
Learning Objectives and Course Syllabus
Grading Scheme
Helpful Tips for Course Completion

Introduction to Deep Learning and Neural Networks

Module Introduction and Learning Objectives
Video: Introduction to Deep Learning
Video: Neurons and Neural Networks
Video: Artificial Neural Networks
Lab: Artificial Neural Networks
Practice Quiz: Introduction to Deep Learning and Neural Networks
Reading: Summary and Highlights: Introduction to Deep Learning and Neural Networks
Graded Quiz: Introduction to Deep Learning and Neural Networks

Basics of Deep Learning

Module Introduction and Learning Objectives
Video: Gradient Descent
Video: Backpropagation
Lab: Backpropagation
Video: Vanishing Gradient
Video: Activation Functions
Lab: Vanishing Gradient and Activation Functions
Practice Quiz: Basics of Deep Learning
Reading: Summary and Highlights: Basics of Deep Learning
Graded Quiz: Basics of Deep Learning

Keras and Deep Learning Libraries

Module Introduction and Learning Objectives
Video: Deep Learning Libraries
Video: Regression Models with Keras
Lab: Regression with Keras
Video: Classification Models with Keras
Lab: Classification with Keras
Practice Quiz: Keras and Deep Learning Libraries
Reading: Summary and Highlights: Keras and Deep Learning Libraries
Graded Quiz: Keras and Deep Learning Libraries

Deep Learning Models

Module Introduction and Learning Objectives
Video: Shallow Versus Deep Neural Networks
Video: Convolutional Neural Networks
Lab: Convolutional Neural Networks with Keras
Video: Recurrent Neural Networks
Video: Transformers
Lab: Transformers with Keras
Video: Autoencoders
Practice Quiz: Deep Learning Models
Reading: Summary and Highlights: Deep Learning Models
Graded Quiz: Deep Learning Models

Final Project

Module Introduction and Learning Objectives
Final Project: Classification and Captioning
Final Project Submission and Evaluation

Course Wrap-Up

Video: Course Wrap-Up
Reading: Congratulations and Next Steps
Reading: Team and Acknowledgments
Copyrights and Trademarks

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General Information:

This course is self paced.
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Recommended Skills Prior to Taking this Course

Working knowledge of Python.
Machine Learning with Python

Fundamentals of Deep Learning using Keras

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