Deep Learning – Homework Assignments

This repository contains the homework submissions for the Deep Learning course. Each assignment covers core deep learning concepts through practical implementation using PyTorch.

Structure

Folder	Description
Homework1/	Intro to PyTorch, tensor operations, linear models
Homework2/	Training deep networks: MLPs, CNNs, YOLO
Homework3/	Generative models: Transformers, GANs, VAEs

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Homework Topics

Homework 1 – Fundamentals

• Topics:
  • PyTorch basics and tensor API
  • Broadcasting and dataset splits
  • Linear regression and linear SVM
  • Optimizing loss functions with SGD

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Homework 2 – Deep Architectures

• Topics:
• Backpropagation and optimizers
• Multilayer Perceptrons (MLPs)
• Convolutional Neural Networks (CNNs)
• Residual connections and YOLO
• Threshold selection and decision boundaries

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Homework 3 – Generative Models & Transformers

• Topics:
• Sequence models and text generation
• Image generation using GANs and VAEs
• Transformers and attention mechanisms