This repository contains implementations of various deep learning models and solutions to challenges in computer vision, speech processing, and natural language processing. It also includes solutions to interview questions and learning materials.
deep-learning-applications/
├── projects/ # Major deep learning projects
│ ├── unet/ # U-Net implementation
│ ├── gan/ # GAN implementations
│ └── hmm-dnn/ # Hidden Markov Model with DNN
├── challenges/ # Coding challenges and solutions
│ ├── classification/
│ ├── text/
│ ├── sequence/
│ └── algorithms/
├── tutorials/ # Learning materials and tutorials
├── interviews/ # Interview questions and solutions
└── notebooks/ # Jupyter notebooks for experiments
Implementation of the U-Net architecture for image segmentation tasks.
Various implementations of GAN architectures for image generation.
HMM-DNN (Hidden Markov Model with Deep Neural Network)
Combined implementation of Hidden Markov Models with Deep Neural Networks.
The challenges directory contains solutions to various deep learning problems:
- Classification challenges
- Text processing tasks
- Sequence modeling problems
- Algorithm implementations
- Python 3.8+
- PyTorch
- NumPy
- Pandas
- Matplotlib
- Clone the repository:
git clone https://github.com/yourusername/deep-learning-applications.git
cd deep-learning-applications- Create a virtual environment (recommended):
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Install dependencies:
pip install -r requirements.txtEach project and challenge has its own README with specific instructions. Please refer to the individual project directories for detailed usage information.
Feel free to submit issues and enhancement requests!
This project is licensed under the MIT License - see the LICENSE file for details.