Got it — here’s a clean, professional GitHub README.md you can directly drop into your repo for this project 👇 (It’s written to match CS 6375 Project 3 and your notebooks: MLP.ipynb, cnn_final.ipynb.)

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# Deep Learning on MNIST and CIFAR-10 (MLP & CNN)

This repository contains my implementation and evaluation of **Multilayer Perceptrons (MLPs)** and **Convolutional Neural Networks (CNNs)** on the **MNIST** and **CIFAR-10** datasets using **PyTorch**.  
This project was completed as **Project 3 for CS 6375: Machine Learning**.

## 📌 Project Objectives
- Implement MLP and CNN models from scratch using PyTorch
- Explore **multiple architectures** with increasing depth
- Perform **validation-based hyperparameter tuning**
- Compare the performance of **MLPs vs CNNs**
- Evaluate final models on held-out **test sets**

The full project specification is provided in the assignment PDF :contentReference[oaicite:0]{index=0}.

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## 📂 Repository Structure

. ├── MLP.ipynb # Multilayer Perceptron experiments (MNIST & CIFAR-10) ├── cnn_final.ipynb # Convolutional Neural Network experiments ├── project_3.pdf # Official assignment description └── README.md

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## 🧠 Models Implemented

### 1️⃣ Multilayer Perceptrons (MLP)
MLPs were trained on **flattened images** from both datasets.

Architectures explored:
- **Shallow MLP**: 1 hidden layer (e.g., 128 units)
- **Medium MLP**: 3 hidden layers (e.g., 512 → 256 → 128)
- **Deep MLP**: 5+ hidden layers

### 2️⃣ Convolutional Neural Networks (CNN)
CNNs were trained directly on image tensors.

Architectures explored:
- **Baseline CNN**: 2 convolutional layers + pooling + FC
- **Enhanced CNN**: Batch Normalization + Dropout
- **Deeper CNN**: 3+ convolutional layers with normalization and dropout

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## ⚙️ Datasets & Preprocessing
- **MNIST** (28×28 grayscale images)
- **CIFAR-10** (32×32 RGB images)

Preprocessing steps:
- Loaded using `torchvision.datasets`
- Pixel normalization applied
- Dataset split into **training / validation / test**
  - MNIST: 50k train / 10k validation
  - CIFAR-10: 45k train / 5k validation

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## 🔍 Hyperparameter Tuning
For each architecture and dataset, multiple configurations were tested using a validation set.

Tuned parameters:
- Learning rate: `{0.01, 0.001, 0.0001}`
- Batch size: `{32, 64, 128}`
- Optimizer: `SGD`, `Adam`
- Dropout rate: `{0.2, 0.5}`

Instead of exhaustive search, **10–12 meaningful configurations** were explored per architecture.  
The best model was selected based on **validation accuracy**, then retrained on combined training + validation data.

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## 📊 Results
Results are reported in the same format as required by the assignment:
- Validation Accuracy (± standard deviation)
- Runtime (minutes)
- Final Test Accuracy

Separate result tables were produced for:
- **MNIST**
- **CIFAR-10**

Key observation:
- CNNs significantly outperform MLPs, especially on **CIFAR-10**, due to their ability to capture spatial features.

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## 🚀 How to Run

### Requirements
```bash
pip install torch torchvision matplotlib numpy

Run MLP Experiments

jupyter notebook MLP.ipynb

Run CNN Experiments

jupyter notebook cnn_final.ipynb

Note: GPU (Google Colab recommended) significantly reduces training time.

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📝 Key Takeaways

• Deeper models do not always guarantee better performance without proper regularization
• CNNs are far more effective than MLPs for image-based tasks
• Batch normalization and dropout improve stability and generalization
• Validation-based tuning is critical for fair model comparison

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📚 References

• PyTorch Documentation: https://pytorch.org/docs/stable/
• MNIST Dataset
• CIFAR-10 Dataset