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- 10:15 - 11:15: Introduction to Reinforcement Learning - Rahul

Reinforcement learning is an important branch of machine learning that deals with how agents evolve over time through interactions with an uncertain environment by taking a sequence of actions while occasionally collecting rewards. The talk introduces to the topic with some examples implemented in Python. It ends with an introduction to -- deep reinforcement learning.

Prerequisites to attend the talk with links for audience to prepare: The talk does not assume any prior mathematical knowledge. However, a conceptual understanding of machine learning is required. Some links -

11:15 - 11:30: Break

11:30 - 12:00 Making Deep Learning Models Robust to Adversarial Attacks - Krishna Kalyan

Deep learning models are considered to be state of art in computer vision for many tasks. However with they can easily be fooled by small intelligent perturbations to input images. The aim of this talk is to introduce the field of security in machine learning. This is an introductory level talk.

Prerequisites - Should know the basics of deep learning Relevant Links - Goodfellow et al: Fast Gradient Sign

12:00 - 12:45: Backpropagation - Shrishty Chandra

Backpropagation is the simplest and most imp concept in deep learning.

We will try to visualize and get a proper understanding of backpropagation during this session.

While we are learning about backpropagation algorithm, we will also learn few new terminologies, like Forward Pass, computation graphs etc.

Please don't go by the weight of the names. They are pretty simple and intuitive. :)

Prerequisites to attend the talk with links for audience to prepare

1. Calculating derivatives

2. Chain rule in calculus

3. linear algebra


1. RSVP opens 7 days before the event.

2. The event is free of cost.

3. Waitlisted participants will receive confirmation notification about a day before the event.

4. If you aren't sure or have other important work to do, please UNRSVP and help others attend.

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