This repository contains a collection of deep learning projects built using Python, TensorFlow, Keras, and PyTorch. These projects demonstrate the implementation of fundamental and advanced concepts such as image classification, object detection, convolutional neural networks (CNNs), and more.
Each project is structured for clarity and includes a Jupyter Notebook (.ipynb) with code, visualizations, and explanations.
The Google Drive link below contains all associated notebooks, trained models, and datasets (if too large for GitHub).
🔗 Drive Folder: Deep Learning Notebooks & Files
- Image Classification using CNN
- MNIST / CIFAR-10 datasets
- Built with Keras and TensorFlow
- Custom Dataset Classification
- Transfer Learning (e.g. using MobileNet or ResNet)
- Face Mask Detection
- Trained model to detect face masks in real-time
- Food Image Classifier
- Multi-class image classification of food items
- Binary Classification Tasks
- E.g., Tumor detection, cat vs dog, etc.
- Python 3.x
- Jupyter Notebook
- TensorFlow / Keras
- PyTorch (in some notebooks)
- OpenCV (for preprocessing and real-time demos)
- Matplotlib, NumPy, Pandas for data manipulation and visualization
git clone https://github.com/Reet-Kamlay/Deep-Learning.git
cd Deep-LearningIt's recommended to use a virtual environment:
python -m venv venv
source venv/bin/activate # On Windows use: venv\Scripts\activate
pip install -r requirements.txt # If providedOr manually install:
pip install numpy pandas matplotlib opencv-python tensorflow kerasjupyter notebookThen open any .ipynb file and run the cells.
- Model files, datasets, and extra notebooks are available in the Google Drive folder.
- Some notebooks may require you to download additional data (check notebook instructions or Drive).
Reet Kamlay
GitHub: @Reet-Kamlay
This repo is intended for educational and experimental use. Contributions and suggestions are welcome!