Assignment can be found here. Join the Slack workspace here.

Dates: 22nd - 24th November. Website.

Assignment can be found here. Join the Slack workspace here.

Assignment can be found here. Join the Slack workspace here.

Dates: 25th - 26th November. Website.

SAiDL's Season of Code gives students to work on cutting-edge AI projects with a strong focus on Open Source. This year's program will run from June to December focusing on a broad range of topics through the following projects -

1. Meta-Learning with JAX
2. Event Vision Library
3. Exploring Deep Learning models for Visual Saliency Prediction

More information on the project goals, related topics, tools mentors and the sign-up form is available here.

Dates: 18th April - 27th April, 2022. Website. Details here.

Speakers: Omatharv Vaidya, Vedant Shah, Sharad Chitlangia, Rajaswa Patil, Alish Dipani. Slides.

Assignment can be found here. Join the Slack workspace here.

Dates: 2nd & 3rd October, 2021. Website. Session recordings.

Taught by: Sharad Chitlangia, Vedant Shah, Rishabh Patra, Soundarya Krishnan, Ishita Mediratta, Anmol Agarwal.

Taught by: Vishwa Shah, Shrey Pandit, Hrithik Nambiar, Yash Bhartia, Sushmit Wani.

SAiDL's Season of Code gives students to work on cutting edge AI projects with a strong focus on Open Source. This year's program will run from August to December focusing on a broad range of topics through the following projects -

1. Causal Inference in Time Series
2. Program Synthesis with Julia
3. Visualisation Library for Vision Transformers
4. Meta Learning with Jax

More information on the projects goals, related topics, tools and mentors is available here. Stay tuned for more details and the release of the sign-up form!

Assignment can be found here.

Dates: 25th & 26th July, 2020. Website. Session recordings.

Assignment can be found here.

SAiDL's Season of Code gives students to work on cutting edge AI projects with a strong focus on Open Source. List of projects -

1. Open Source python package Adversarial NLP
2. Deep Contextual Bandits
3. Computer Vision for Sports Analytics
4. Knowledge Distillation library
5. Twitter Feed Distillation
6. Benchmarking Causal inference and Reinforcement learning algorithms on dynamic environments
7. Deep Learning package for Time Series Modelling
8. Exploring Applications of Spiking Neural Networks

More information on the project deliverables, description and mentors is available here.

A Quark Summer Project on learning the fundamentals of deep learning and how they are applied in Computer Vision, Natural Language Processing and Reinforcement Learning.

Assignment can be found here.

Taken by Rajaswa Patil and Pranav Mahajan. The course included introduction to fundamental concepts of Machine Learning and Deep learning and hand-on experience through projects and self-organized Kaggle competitions. Batch strength: 120 students. Course handout.

Course taken by Ashwin Vaswani, Rijul Ganguly. Course handout.

Learning to play games with RL, Computer Vision, Financial Market Modelling, Machine Learning and AI & Cognitive Neuroscience.

Assignment can be found here.

Taken by Mehul Rastogi, Sharad Chitlangia, Rijul Ganguly and Ajay Subramanian. Slides and resources can be found here.

Assignment can be found here.

Taken by Alish Dipani, Mehul Rastogi, Sharad Chitlangia, Rijul Ganguly. Slides and resources can be found here.

Induction assignment.