Deep Learning Lab Activities

A comprehensive collection of educational deep learning programs demonstrating fundamental concepts in computer vision, natural language processing, and sequence modeling.

🚀 Quick Start - Web Interface (Recommended)

The easiest way to run and visualize the labs is through the web interface:

1. Install Dependencies

pip install -r requirements.txt
cd web_interface
pip install -r requirements.txt

2. Start the Web Server

cd web_interface
python app.py

3. Open in Browser

Navigate to http://localhost:5000 in your web browser.

4. Run Labs

• Browse all 10 labs from the dashboard
• Click on any lab card to open its interface
• Click "Run Lab" to execute
• View real-time console output
• See generated visualizations automatically

Features:

• Interactive web dashboard with Full Labs, Lite Labs, and Documentation sections
• Real-time console output display
• Automatic visualization of results
• Run/Stop controls for each lab
• Built-in documentation viewer
• Modern dark theme with responsive design

For detailed web interface documentation, see web_interface/README.md

🐍 Using Virtual Environment (Recommended)

It's highly recommended to use a virtual environment to avoid dependency conflicts:

Option 1: Using venv (Python built-in)

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On macOS/Linux:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt
cd web_interface
pip install -r requirements.txt
cd ..

# Run web interface
cd web_interface
python app.py

Option 2: Using conda

# Create conda environment
conda create -n dl-lab python=3.9

# Activate environment
conda activate dl-lab

# Install dependencies
pip install -r requirements.txt
cd web_interface
pip install -r requirements.txt
cd ..

# Run web interface
cd web_interface
python app.py

Deactivating Virtual Environment

When you're done:

# For venv:
deactivate

# For conda:
conda deactivate

Benefits of Virtual Environment:

• Isolated dependencies from system Python
• Prevents version conflicts with other projects
• Easy to reproduce exact environment
• Clean uninstall by simply deleting the environment

📚 Overview

This repository contains 10 self-contained lab activities designed for academic learning. Each lab includes:

• Well-commented, production-quality code
• Clear learning objectives
• Sample datasets or data generation
• Training and evaluation pipelines
• Visualization of results
• Execution time under 5 minutes on standard hardware

🗂️ Lab Activities

Lab 1: Basic Image Processing Operations

Directory: labs_full/lab_01_image_processing/ | Lite: labs_lite/lab_01_image_processing/

• Histogram equalization
• Thresholding techniques
• Edge detection (Sobel, Canny)
• Data augmentation
• Morphological operations

Lab 2: CIFAR-10 Classifiers

Directory: labs_full/lab_02_cifar10_classifiers/ | Lite: labs_lite/lab_02_cifar10_classifiers/

• KNN classifier implementation
• 3-layer neural network classifier
• SVM/Softmax classifier comparison

Lab 3: Batch Normalization and Dropout Study

Directory: labs_full/lab_03_batchnorm_dropout/ | Lite: labs_lite/lab_03_batchnorm_dropout/

• Effect of batch normalization on training
• Impact of dropout on overfitting
• Comparative analysis with visualizations

Lab 4: Image Labeling Tools Demonstration

Directory: labs_full/lab_04_labeling_tools/ | Lite: labs_lite/lab_04_labeling_tools/

• Synthetic image generation with annotations
• Format conversions (COCO, YOLO, Pascal VOC)
• Annotation visualization
• Statistics generation

Lab 5: Image Segmentation with UNet

Directory: labs_full/lab_05_segmentation/ | Lite: labs_lite/lab_05_segmentation/

• UNet architecture implementation
• Comparison with simple CNN baseline
• Evaluation metrics (IoU, Dice coefficient)
• Synthetic segmentation dataset

Lab 6: Object Detection with YOLO

Directory: labs_full/lab_06_object_detection/ | Lite: labs_lite/lab_06_object_detection/

• YOLO-style object detector
• Bounding box prediction and classification
• Non-Maximum Suppression (NMS)
• Detection visualization

Lab 7: Image Captioning with RNN/LSTM

Directory: labs_full/lab_07_image_captioning/ | Lite: labs_lite/lab_07_image_captioning/

• CNN encoder + RNN/LSTM decoder
• Comparison between Vanilla RNN and LSTM
• Caption generation
• Synthetic image-caption pairs

Lab 8: Chatbot with Bi-directional LSTM

Directory: labs_full/lab_08_chatbot/ | Lite: labs_lite/lab_08_chatbot/

• Bi-directional LSTM for intent classification
• Comparison with unidirectional LSTM
• Response generation
• Interactive demo

Lab 9: Time Series Forecasting with LSTM

Directory: labs_full/lab_09_time_series/ | Lite: labs_lite/lab_09_time_series/

• LSTM and GRU for forecasting
• Multi-step ahead prediction
• Comparison with baseline
• Evaluation metrics (MSE, MAE, RMSE, MAPE)

Lab 10: Sequence to Sequence Learning

Directory: labs_full/lab_10_seq2seq/ | Lite: labs_lite/lab_10_seq2seq/

• Encoder-decoder architecture
• Attention mechanism
• Machine translation (synthetic language)
• Beam search decoding

💻 Alternative: Command Line

If you prefer running labs directly from the command line:

Prerequisites

• Python 3.8 or higher
• pip package manager
• (Optional) CUDA-capable GPU for faster training

Installation

1. Clone or download this repository
2. Install dependencies:

pip install -r requirements.txt

Running Individual Labs

Each lab is self-contained with an executable Python program. Navigate to the lab directory and run:

# Lab 1: Image Processing
cd labs_full/lab_01_image_processing
python image_processing.py

# Lab 2: CIFAR-10 Classifiers
cd labs_full/lab_02_cifar10_classifiers
python cifar10_classifiers.py

# Lab 3: Batch Normalization & Dropout
cd labs_full/lab_03_batchnorm_dropout
python batchnorm_dropout_study.py

# Lab 4: Labeling Tools Demo
cd labs_full/lab_04_labeling_tools
python labeling_demo.py

# Lab 5: Image Segmentation
cd labs_full/lab_05_segmentation
python segmentation_demo.py

# Lab 6: Object Detection
cd labs_full/lab_06_object_detection
python object_detection_demo.py

# Lab 7: Image Captioning
cd labs_full/lab_07_image_captioning
python image_captioning_demo.py

# Lab 8: Chatbot
cd labs_full/lab_08_chatbot
python chatbot_demo.py

# Lab 9: Time Series Forecasting
cd labs_full/lab_09_time_series
python time_series_demo.py

# Lab 10: Sequence to Sequence
cd labs_full/lab_10_seq2seq
python seq2seq_demo.py

Or run all labs sequentially:

cd labs_full
bash run_all_labs.sh

For lightweight versions with faster execution:

cd labs_lite
bash run_all_labs.sh

Note: Each program creates an output/ directory in its lab folder with visualizations and results.

📦 Dependencies

Core libraries used across labs:

• PyTorch: Deep learning framework
• TensorFlow/Keras: Alternative framework for some labs
• OpenCV: Image processing
• NumPy: Numerical computations
• Matplotlib/Seaborn: Visualization
• scikit-learn: Classical ML algorithms
• Pillow: Image handling
• pandas: Data manipulation

See requirements.txt for complete list with versions.

📊 Expected Execution Times

All labs are designed to complete within 5 minutes on standard hardware (CPU):

Lab	Program	Execution Time	Notes
Lab 1	image_processing.py	< 30 seconds	Image processing operations
Lab 2	cifar10_classifiers.py	2-3 minutes	KNN, SVM, 3-layer NN training
Lab 3	batchnorm_dropout_study.py	2-3 minutes	4 model variants comparison
Lab 4	labeling_demo.py	< 30 seconds	Synthetic data generation
Lab 5	segmentation_demo.py	3-4 minutes	UNet + baseline training
Lab 6	object_detection_demo.py	3-4 minutes	YOLO-style detector training
Lab 7	image_captioning_demo.py	3-4 minutes	RNN + LSTM training
Lab 8	chatbot_demo.py	2-3 minutes	BiLSTM + LSTM training
Lab 9	time_series_demo.py	2-3 minutes	LSTM + GRU forecasting
Lab 10	seq2seq_demo.py	3-4 minutes	Seq2Seq + Attention training

Total time for all labs: ~25-30 minutes

With GPU: Execution times can be 2-5x faster depending on GPU model.

Lite versions: Execute 2-3x faster with reduced dataset sizes and epochs.

🎓 Learning Objectives

By completing these labs, you will:

1. Understand fundamental image processing techniques
2. Implement and compare different classification approaches
3. Master regularization techniques in neural networks
4. Learn data annotation workflows for computer vision
5. Build semantic and instance segmentation models
6. Implement modern object detection architectures
7. Create image captioning systems with RNNs and LSTMs
8. Develop conversational AI with bi-directional LSTMs
9. Apply deep learning to time series problems
10. Understand sequence-to-sequence architectures

📝 Repository Structure

dl-lab/
├── web_interface/         # Web-based interface (recommended)
│   ├── app.py            # Flask application
│   ├── templates/        # HTML templates
│   └── requirements.txt  # Web interface dependencies
├── labs_full/            # Full-featured lab programs
│   └── lab_XX_name/
│       ├── README.md     # Lab-specific instructions
│       ├── program.py    # Main executable
│       └── output/       # Generated results (created on run)
├── labs_lite/            # Lightweight versions (faster execution)
│   └── lab_XX_name/
│       ├── program_lite.py
│       └── output/
├── docs/                 # Additional documentation
└── requirements.txt      # Main dependencies

All programs:

• Are fully self-contained and executable
• Generate synthetic data or download datasets as needed
• Create visualizations and save results in output/ directory
• Include comprehensive comments and documentation
• Print progress and results to console
• Available in both full and lite versions

🔧 Troubleshooting

Common Issues

Import Errors:

pip install --upgrade -r requirements.txt

CUDA Out of Memory:

• Reduce batch size in the code
• Use CPU mode by setting device = 'cpu'

Slow Execution:

• Ensure you're using GPU if available
• Reduce dataset size or number of epochs
• Check system resources

📚 Documentation

For more detailed information, see:

• Web Interface Guide - Complete web interface documentation
• Installation Guide - Detailed setup instructions
• Dataset Setup Guide - Dataset download and management
• Getting Started - Beginner's guide
• Advanced Tab Guide - Advanced features

📚 Additional Resources

• PyTorch Documentation
• TensorFlow Documentation
• OpenCV Tutorials
• Deep Learning Book

🤝 Contributing

This is an educational repository. Feel free to:

• Report issues or bugs
• Suggest improvements
• Add more examples or variations

📄 License

This project is created for educational purposes. Feel free to use and modify for learning.

✨ Acknowledgments

These labs are designed for academic learning and incorporate best practices from:

• Stanford CS231n: Convolutional Neural Networks
• Stanford CS224n: Natural Language Processing
• Fast.ai courses
• PyTorch and TensorFlow tutorials

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Note: All programs are designed to be self-contained and educational. They prioritize clarity and learning over production optimization.

Last Updated: March 2026