The DeepLearning-Hub repository is a centralized resource for deep learning enthusiasts, researchers, and practitioners. It includes:

📚 DeepLearning-topics: Hands-on Jupyter notebooks covering fundamental to advanced deep learning topics.

🏗 Pre-trained Models in subtopics: Implementations of CNNs, RNNs, Transformers, GANs, and more.

📊 Datasets: Sample datasets for computer vision, NLP, and time-series tasks.

⚡ Notebooks: Scripts for data preprocessing, model training, and evaluation etc.

🚀 Deployment: Guides and code for deploying DL models using FastAPI, Flask, and cloud services.

This hub is designed to help learners, developers, and researchers explore deep learning through structured content, real-world implementations, and collaborative contributions. 🚀

🚀 DeepLearning-Hub

📌 Overview

DeepLearning-Hub is a comprehensive repository for deep learning enthusiasts, featuring tutorials, pre-trained models, datasets, utilities, and deployment guides.

📂 Repository Structure

DeepLearning-Hub/
│── README.md                # Project overview
│── requirements.txt         # Dependencies
│── DeepLearning-topics/sub-topics               # Jupyter notebooks for learning
│── /sub-topics/models/                  # Pre-trained deep learning models
│── datasets/                # Sample datasets
│── Notebooks/             # Custom experiments and benchmarking
│── deployment/              # Model deployment scripts (FastAPI, Flask, etc.)
│── references/              # Research papers, blogs, and resources
│── CONTRIBUTING.md          # Contribution guidelines
│── LICENSE                  # License information

🚀 Getting Started

1️⃣ Clone the Repository

git clone https://github.com/Mahadevkharmate/DeepLearning-Hub.git
cd DeepLearning-Hub

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Explore Tutorials

Navigate to the tutorials/ folder and start with 01_intro_to_dl.ipynb.

🧠 Topics Covered

✅ Neural Networks (MLP, CNN, RNN, Transformers)
✅ TensorFlow, PyTorch, Keras Implementations
✅ Model Training, Optimization & Hyperparameter Tuning
✅ Image, Text, and Time-Series Analysis
✅ GANs, Reinforcement Learning, and Autoencoders
✅ Model Deployment (FastAPI, Flask, AWS)

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🏆 Contributing

Contributions are welcome! Check the CONTRIBUTING.md file for details.

⭐ Star & Support

If you find this repo useful, give it a ⭐ and contribute to its growth!

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Happy Learning! 🚀