# Deep Learning

## Overview

In the last decade due to the availability of cheap computational power and relevant software tools, several neural networks are explored to advance state-of-the-art of
many hard AI problems. These architectures have successfully advanced various areas such as `image searching`

,
`natural language understanding`

, `medical applications`

,
`autonomous vehicles`

etc. All these problems rely on efficient, accurate and robust
solutions for basic vision tasks such as `image classification`

, `localization`

and
`detection`

. In this course students will be given an exposure to the details of `neural networks`

as well as deep learning architectures and to develop end-to-end
models for such tasks. Students will learn to implement, train and debug their own neural networks. This is a project oriented practical course in which every student
has to develop a complete working model to solve some real-world problem.

## Navigation

## Prerequisites

This course has no prerequisites.

## Textbooks

Title | Author(s) | Edition |
---|---|---|

Deep Learning | Ian Goodfellow, Yoshua Bengio & Aaron Courville | (2016) |

Dive into Deep Learning | Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola | (2020) |