Course Description
Deep learning is the core technology behind artificial intelligence that is transforming our world.
In this course, you’ll cover Convolutional and Recurrent Neural Networks, Generative Adversarial Networks, Data preparation, Deployment, and more. You’ll use R, and have access to GPUs to train models faster. This is the ideal point-of-entry into the field of AI.
What am I going to get from this course?
Learning and understanding of Deep Learning technologies and how it can be applied to research.
Prerequisites and Target Audience
What will students need to know or do before starting this course?
Outside of the computer science knowledge, it's a beginner-friendly program that will take you from beginner’s level to expert level understanding of Deep Learning technologies.
Who should take this course? Who should not?
Created specifically for students who are interested in research, machine learning, Artificial Intelligence, and/or deep learning, and who have a basic knowledge of computer science, including R.
Curriculum
Lecture 1
Deep Learning-Definition
Lecture 2
Deep Learning- Examples at Work
Explain why does deep learning even matter.
Lecture 3
Deep Learning- Overview
Examples of how deep learning is used in different industries.
Lecture 4
Deep Learning- Why they Work
How deep learning works, a basic understanding of the tech.
Lecture 5
A Brief History- Part 1
Computers can now play games, who taught them?
Lecture 6
Brief History- part 2
Growth in deep learning conferences and online course continues to soar because of its wide applicability .
Lecture 7
Brief History- part 3
Who are the founding founders of Deep learning?
Lecture 8
Brief History- part 4
Long short-term memory was proposed.
Module 4: Deep Learning Models
Lecture 9
Deep Learning Models
Here a few classic deep learning models - Convolutional Neural Network, Recurrent Neural Network.
Lecture 10
Recurrent Neural Network
The power of Recurrent Neural Network.
Lecture 11
Reinforcement ( Deep) Learning
More classic deep learning models - Auto-encoders, Reinforcement Learning .
Lecture 12
Deep Learning Challenges
Deep Learning is no doubt a powerful tool but they are some challenges lying ahead.
Lecture 13
Deep Learning Challenges- Contd
Deep Learning is no doubt a powerful tool but they are some challenges lying ahead.
Module 6: Deep Learning App (1/4)
Lecture 14
Speech Recognition
Learn to build a deep learning App that converts audio and voice into written text.
Lecture 15
Data Preparation (Case study)
Data preparation a case study.
Module 7: Deep Learning App (2/4)
Lecture 16
Image Recognition
An introduction into how computer recognizes faces.
Module 8: Deep Learning App (3/4)
Lecture 17
Breast Cancer Detection
How breast cancer can be detected in a woman’s breast given a a certain data-set.
Module 9: Deep Learning App (4/4)
Lecture 18
Drug Discovery
This module will show how Deep learning can be applied into drug discovery.
Lecture 19
Why Deep Learning is Relevant
Much has been said of Deep learning but we are only scratching the surface.
