This repository contains a collection of deep learning implementations, experiments, and models developed for learning, research, and practical applications. It covers fundamental to advanced concepts in deep learning, including neural networks, model training, evaluation, and optimization techniques.
The repository is structured to support modular development and experimentation across different problem domains.
- Implement core deep learning algorithms from scratch and using frameworks
- Explore various neural network architectures
- Perform experiments on real-world datasets
- Understand model performance and optimization techniques
- Build a strong foundation in deep learning concepts
- Implementation of neural network models
- Training and evaluation pipelines
- Dataset handling and preprocessing utilities
- Modular and extensible code structure
- Experimentation with different architectures and hyperparameters
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Language: Python
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Frameworks/Libraries:
- TensorFlow / Keras or PyTorch
- NumPy
- Pandas
- Matplotlib / Seaborn
DeepLearning/
│
├── datasets/ # Dataset files or loaders
├── models/ # Model architectures
├── notebooks/ # Jupyter notebooks for experiments
├── scripts/ # Training and evaluation scripts
├── utils/ # Helper functions and utilities
├── results/ # Output results and logs
├── requirements.txt # Dependencies
└── README.md
- Python 3.x
- pip or conda
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Clone the repository:
git clone https://github.com/yeswanthkutty001-cyber/Deep_Learning_Frameworks.git cd DeepLearning -
Install dependencies:
pip install -r requirements.txt
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Run Jupyter notebooks for experimentation:
jupyter notebook -
Execute training scripts:
python scripts/train.py -
Evaluate models:
python scripts/evaluate.py
This repository may include experiments such as:
- Image classification
- Neural network training from scratch
- Optimization techniques (SGD, Adam, etc.)
- Model comparison and evaluation
Results and outputs such as trained models, logs, and performance metrics are stored in the results/ directory.
- Add more advanced architectures (CNNs, RNNs, Transformers)
- Integrate larger datasets
- Improve training performance and optimization
- Add deployment support (API or web interface)
- Academic learning and coursework
- Deep learning experimentation
- Prototyping AI models
- Interview preparation and portfolio projects
This project is intended for educational and research purposes. Modify and extend as needed.
Developed as part of deep learning exploration and practical implementation work.