Deep Learning is a subfield of machine learning mostly concerned with concepts and techniques built on top of the artificial neural network which in turn was inspired by the structure and functionality of human brains. This branch of machine learning is increasingly gaining popularity as deep learning systems are taking over all artificial intelligent tasks, ranging from image classification, language modeling, machine translation, playing games, autonomous vehicle driving, speech recognition, cancer detection and numerous other applications and is behind many recent advances in Artificial Intelligence (AI). In this course you will gain both theoretical and practical knowledge of deep learning concepts and techniques. So, welcome aboard!

Course objectives

By the end of the course you are expected to gain the following skills:

1. Understand fundamentals of artificial neural network, and deep neural networks.
2. Develop an understanding on how to train a neural network.
3. Determine how a deep neural network can be designed, and implemented to solve real world problems.
4. Demonstrate an in-depth understanding of one/more concepts introduced in the deep learning course through a final project.

Prerequisites

1. The graduate standing.

Topics covered

1. Introduction to Deep Learning
2. AI, ML, DL Review
3. Feed forward Artificial Neural Nets
4. Ethical considerations for designing AI systems
5. Convolution Neural Networks
6. Recurrent Neural Networks
7. Gradient Descent review
8. Backpropagation algorithm in RNN
9. Unsupervised Deep Learning
   1. Semantic segmentation with U-net
   2. Generative Adversarial networks (GANs)
   3. Auto-encoder
10. Deep Reinforcement Learning
11. Dropouts, Batch normalization
12. Tensorboard
13. Ways to improve ANN training