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Student AI & deep learning hackathonJuly 7, 2024

Deep Learning Codefest

A 10-hour codefest bringing mixed-cohort BCS teams together to build deep-learning models for safer, more efficient autonomous vehicles.

Student teams working at laptops throughout the Deep Learning Codefest venue
The 10-hour Deep Learning Codefest at Aranya Boutique Hotel

EVENT OVERVIEW

A clear purpose, translated into a complete participant journey.

Event type
Student AI & deep learning hackathon
Format
Physical
Duration
10-hour continuous hackathon
Audience
Bachelor of Computer Science students across senior, junior and fresher cohorts
Location
Aranya Boutique Hotel, Shivapuri Block, Kathmandu
Event date
July 7, 2024

01 / THE BRIEF

What the institution needed

The challenge invited students to move beyond classroom theory, apply deep-learning knowledge to a real-world problem, demonstrate their technical ability and build practical AI and machine-learning experience in a competitive setting.

02 / OUR APPROACH

Design the journey, not only the event

The program began with a 10-day AI and machine-learning bootcamp followed by cross-cohort team registration, coding-environment setup, mentor briefings and a final environment test. On hackathon day, teams researched, coded, tested and presented autonomous-vehicle models before a judging panel.

03 / WHAT WE HANDLED

Connected work across the program.

  • 0110-day AI & ML bootcamp
  • 02Cross-cohort team formation
  • 03Coding environment setup
  • 04Mentor briefing
  • 05Challenge delivery
  • 0610-hour event production
  • 07Participant engagement
  • 08Presentation workflow
  • 09Judging coordination
  • 10Awards and recognition

04 / PROGRAM TIMELINE

From preparation to recognition.

  1. 01

    Bootcamp

    A 10-day, three-phase AI and machine-learning bootcamp moved participants from theory toward practice.

  2. 02

    Team registration

    Senior students, juniors and freshers formed cross-cohort teams and registered against the event rules.

  3. 03

    Environment setup

    The coding platform, links, internet access, layout and execution pipelines were configured and mentors were briefed.

  4. 04

    Environment test

    A final technical test confirmed that each team could work reliably before the competition began.

  5. 05

    Hackathon day

    Teams spent 10 continuous hours researching, coding and testing deep-learning models for autonomous vehicles.

  6. 06

    Presentations & judging

    Teams presented to judges who assessed accuracy, innovation, code quality and presentation.

  7. 07

    Awards

    The event recognized the best model, most innovative solution, outstanding presentation and best team spirit.

05 / PHOTOGRAPHY

Inside the event.

06 / OUTCOMES

What the program made possible.

01

Applied deep learning

Teams translated classroom knowledge into models that considered traffic patterns, road conditions, pedestrians, obstacles and vehicle behaviour.

02

Rigorous technical practice

Participants developed and tested their models against multiple datasets and scenarios before presenting their final approach.

03

Best Model

Team Hacktastic received the award for Best Model.

04

Innovative Solution

Team Hexaminner received the award for Innovative Solution.

05

Best Team Spirit

Team CodeX received the award for Best Team Spirit, alongside a separate Outstanding Presentation award.

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READY WHEN YOU ARE

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