The Deep Learning Bootcamp (3rd Edition) consists of five sessions designed to progressively build students’ practical understanding of deep learning—from foundational Python skills to advanced applications. In this series, you'll gain an intuitive understanding of Deep Learning with hands-on technical works. It’s a great opportunity to upskill and network with peers from various disciplines who share a passion for Python and AI.
Session 2: Deep Learning for Regression and Classification Covers PyTorch fundamentals and demonstrates how to design and train models for regression and classification on tabular data. Includes live coding and guided exercises.
Join us and take the first step towards integrating Deep Learning into your research!
📅 Date: 10 Nov 2025
🕑 Time: 2:30 PM – 5:30 PM
📍 Venue: The ARC TR+30
1. Bring your laptop.
2. Tea break and snack are provided.
Session 1 – Deep Learning Essentials
📅 Date: 24 Oct 2025
🕑 Time: 2:00 PM – 5:00 PM
📍 Venue: The ARC TR+04
Introduces Python and key packages (NumPy, SciPy, Pandas) essential for data handling and model development. Participants build a strong foundation for applying deep learning techniques in later sessions.
Session 2 – Deep Learning for Regression and Classification
📅 Date: 10 Nov 2025
🕑 Time: 2:30 PM – 5:30 PM
📍 Venue: The ARC TR+30
Covers PyTorch fundamentals and demonstrates how to design and train models for regression and classification on tabular data. Includes live coding and guided exercises.
Session 3 – Deep Learning for Images
📅 Date: 17 Nov 2025
🕑 Time: 2:00 PM – 5:00 PM
📍 Venue: The ARC TR+4
Focuses on computer vision applications, where participants learn to process and classify image datasets using convolutional neural networks (CNNs).
Session 4 – Natural Language Processing (NLP)
📅 Date: To be decided
🕑 Time: To be decided
📍 Venue: To be decided
Covers deep learning models for text and sequence data. Participants explore sentiment analysis and time-series prediction while learning sequence modeling principles.
Session 5 – Reinforcement Learning (RL)
📅 Date: To be decided
🕑 Time: To be decided
📍 Venue: To be decided
Concludes the series with the fundamentals of Reinforcement Learning, introducing agents, environments, rewards, and policies through practical examples and simulations.