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README.md

CAIC Summer of Tech '25

Kickoff & Problem Statement (CS Research)
By: ARIES X ACES ACM

🌱 What is CAIC Summer of Tech?

CAIC Summer of Tech is a 5-week learning initiative by the Co-curricular and Academic Interaction Council (CAIC) designed to help freshers and beginners explore tech domains without pressure or prerequisites.

Inspired by Inter-IIT Tech Meet problem statements, it offers guided tracks in:

  • Generative AI
  • CS Research
  • Cybersecurity
  • Quant & Finance
  • And more...

Each track includes:

  • ✅ Bite-sized weekly content
  • ✅ Beginner-friendly, hands-on projects
  • ✅ Mentorship from senior students

No prior experience? No stress. You’ll learn by building — not before building.

📅 General Guidelines

  • Duration: 5 weeks (Starting from 25 May, 2025)
  • Weekly Deliverables: Tasks designed to be manageable and non-overwhelming
  • Communication & Updates:
    Join the official WhatsApp group: Join Now

🧑‍💻 Participation Format

  • Individual learning in the early phase
  • Team formation happens later during project implementation
  • Weekly progress reports will be submitted individually

📦 Content Format

  • Weekly releases with:
    • 📹 Explanatory videos
    • 📝 Notes
    • 💻 Code samples
  • No prior knowledge required — all explained in simple terms
  • Seniors and mentors available for doubt resolution

✅ Expectations

  • Submit progress regularly at checkpoints
  • Show up, ask questions, and stay curious — no need to stress
  • Projects submitted at the end of 5 weeks

🏆 Evaluation & Recognition

  • Learning > Competition
  • Exceptional work may lead to:
    • 🎖 Shortlisting for Inter-IIT Tech Meet
    • 🎉 Shoutouts & Prizes

🤝 Code of Conduct

  • Respect others’ learning journeys
  • Collaborate ethically — no plagiarism
  • Be open, helpful, and supportive

🧠 CS Research Track – Problem Statement

🔍 Track Overview

In this track, you’ll dive into the world of Neural Networks, with a special focus on Convolutional Neural Networks (CNNs) — a foundational architecture for image and pattern recognition.

You’ll go from training models using real datasets to building your own CNN from scratch, gaining both practical skills and theoretical understanding.

📚 How It Works

The CS Research track spans 5 weeks:

  • Week 1–2:

    • Basics of neural networks
    • Intro to CNNs and how they work
    • All concepts explained in beginner-friendly terms

  • Week 3–4:

    • Practice: train CNN models on real datasets
    • Learn how to evaluate performance
    • Modify architecture and observe outcomes

  • Week 5:

    • Build your own CNN from scratch
    • Experiment by tuning hyperparameters and improving accuracy

All resources and tutorials will be released gradually, so you’re never overwhelmed.

🎯 Final Goal

✅ Implement a working CNN model on a given dataset
✅ Understand how it learns, predicts, and improves
✅ Submit a well-documented notebook or script by the end

🚀 Stretch Goals (Optional)

  • Try optimization techniques like learning rate scheduling or Adam optimizer
  • Add regularization (e.g., Dropout, L2) to prevent overfitting
  • Compare multiple versions of your model and identify what improves accuracy
  • Explore simple data augmentation for better generalization

We’re excited to have you onboard. Let’s make research approachable and fun!

Happy learning! 🧠✨