QIP Sponsored
Deep Learning: Theory & Practice

December 12 – 17, 2018

Department of Computer Science and Engineering
Indian Institute of Technology (BHU) Varanasi – 221005, U.P. , India



Deep learning is a new area of Machine Learning research. Deep Learning has been introduced with the objective of moving Machine Learning closer to one of its original goals i.e. Artificial Intelligence. Deep learning is one of today's most rapidly growing technologies, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in deep learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics and drug design. Each of these domains has great research potential. This course includes both theoretical lectures and hands on practice sessions on topics related to deep learning to teach and do research in this emerging field of study.


The topics that are going to be covered are as follows:

1. Deep Feedforward Networks
2. Regularization for Deep Learning
3. Optimization for Training Deep Models
4. Convolutional Networks
5. Sequence Modeling: Recurrent & Recursive Nets
6. Practical Methodology
7. Deep Learning Research
8. Applications of Deep Networks

Notice about Logistics [PDF]

Tentative Schedule [PDF]

Tools for ML & DL [PDF]

Clustering, Classification and Regression Practice [PDF]

CNN & RCNN Practice [PDF]

RNN & LSTN Practice [PDF]

The Study Material by Prof. K.K Shukla [Link]

The Study Material by Prof. T. Som [PDF]

Frequently Asked Question (FAQ)