Applications of Computer Vision and Deep Learning

RKMVERI CS/BDA | Spring 2026

Instructor: Jimut Bahan Pal
Credits: 3
Total Hours: 50 hrs

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Course Details

Attendance Policy: Credited students must maintain at least 75% attendance to receive a grade.

Comprehensive Curriculum

Commencing with foundational Python3, we advance toward pragmatic implementation using OpenCV and PyTorch, bridging the gap between theoretical math and deployment-ready applications.

Hands-on Learning

This tutorial-driven program blends theory with immersive tutorials. It is designed for those enthusiastic about applying classical and cutting-edge deep learning algorithms to real-world scenarios.

Research & Industry Focus

Participants attain mastery in designing models for classification, segmentation, and LLMs, critical skills for creating real-world applications in both industry and research domains.

Course Curriculum

  • • Overview & PyTorch Basics
  • • Theory: Softmax, Cross-Entropy
  • • Building Classification Pipelines
  • • DNN Parameter Calculation
  • • Variational Auto-Encoders (VAE)
  • • DL-based Segmentation Methods
  • • CNNs (DLNLP Guest Lecture)
  • • Transformers & BERT
  • • Transformer-based Models & Tricks
  • • Large Language Models (LLMs)
  • • LLM Training & Tuning
  • • LLM Reasoning & Evaluation
  • • Agentic LLMs
  • • Current Trends in AI

* We actively use Google Space for communication and slide sharing. Join the Google Space here.

Schedule & Materials

Lecture videos are updated after each session. [View Mirrors]

Total Lecture Covered: 11 hrs
Date Time Topic Materials
18-01-2026 7:00 - 12:00 PM Lec-1: Overview & General Introductions (Including previous two meetings)
25-01-2026 8:30 - 11:30 PM Lec-2: Basics of Pytorch and Activation functions in DNNs
02-01-2026 8:30 - 11:30 PM Lec-3: Perceptron, MLP and Pytorch Pipeline for classification