Welcome to Deep learning course. What is Deep learning? It is actually a subfeld of machine learning mostly concerned with concepts and techniques built on top of the artifcial neural networi which in turn was inspired by the structure and functionality of human brain. This branch of machine learning is increasingly gaining popularity as deep learning systems are taiing over all artifcial intelligent tasis, ranging from image classifcation, language modeling, machine translation,cplaying games, autonomous vehicle driving, speech recognition, cancer detection and numerous other applications and dominating over most competing systems. In this course you will gain both theoretical and practical inowledge 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

For undergraduate students:

1. MATH 3195
2. CSCI 3412

For graduate students:

1. The graduate standing.

Topics covered

1. Introduction to Deep learning
2. Machine learning Review
3. Feed forward Deep networks, backprop, regularization, optimization, and compute framework
4. Convolutional Neural Network
5. Word embeddings and Recurrent Neural Network
6. Deep Reinforcement Learning
7. Unsupervised DNN