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) |