🧠✨ TensorFlow Projects Hub

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Welcome to the TensorFlow Projects Hub — a curated, continually expanding repository of deep learning projects built with TensorFlow and Keras.

This hub is crafted for practitioners, researchers, and students to learn, teach, replicate, and innovate on real-world datasets using battle-tested architectures and educational clarity.

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📚 About This Repository

This repo offers:

• 📈 End-to-End ML Projects – Regression, classification, image recognition
• 🧩 Paper Replications – ResNet, DenseNet, Wide ResNet in TensorFlow
• 🧪 Educational Models – Simplified for fast learning and experimentation
• 📊 Full Pipeline Visualizations – EDA, training metrics, ROC, confusion matrix
• 📎 Best Practices – Preprocessing, training, evaluation, modular design

All projects are well-documented and beginner-friendly without sacrificing architectural depth.

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📂 Project Structure

TensorFlow-Projects-Hub/
├── classification/
│   ├── wine_classification_tensorflow.py
│   ├── mnist_classification_resnet.py
│   └── cifar10_wide_resnet.py
├── regression/
│   └── california_housing_regression.py
├── paper_replications/
│   ├── resnet_paper_replicated.py
│   ├── wide_resnet_paper_replicated.py
│   └── densenet_paper_replicated.py
├── notebooks/
│   └── [Jupyter notebooks for interactive use]
├── README.md
└── requirements.txt

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🚀 Projects Overview

✅ Classification Projects

• Wine Dataset: EDA, metrics, ROC, confusion matrix
• ResNet (MNIST): From Deep Residual Learning for Image Recognition
• Wide ResNet (CIFAR-10): Based on Wide Residual Networks

📉 Regression Projects

• California Housing Price Prediction: Full regression pipeline + residual analysis

🧠 Paper Replications

• 📘 ResNet – Original residual blocks (He et al.)
• 📘 Wide ResNet – Widened blocks (Zagoruyko et al.)
• 📘 DenseNet – Dense connectivity (Huang et al.)

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🧠 Philosophy

"Don’t just use machine learning—understand it."

We aim to demystify deep learning through clarity, visualization, and modularity. By recreating advanced architectures in digestible form, we make ML accessible without diluting rigor.

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🛠️ Installation

Clone the repository and install dependencies:

git clone https://github.com/your-username/tensorflow-projects-hub.git
cd tensorflow-projects-hub
pip install -r requirements.txt

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🧪 Requirements

• TensorFlow >= 2.8
• matplotlib, seaborn
• scikit-learn, pandas, numpy
• tqdm (optional for progress bars)

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📌 Roadmap

• Classification and regression baselines
• Replicate ResNet, DenseNet, Wide ResNet
• SHAP and Grad-CAM visualizations
• NLP models (RNNs, Transformers)
• Pre-trained weights and model checkpoints

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🤝 Contributing

We welcome all contributions! 🎉 Submit PRs, request new features, or suggest papers to replicate. See CONTRIBUTING.md for guidelines.

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📜 License

This project is licensed under the MIT License. Free to use, modify, and share — no strings attached.

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💡 Acknowledgements

This work draws on:

• 🧠 Research by He et al., Zagoruyko et al., Huang et al.
• 📘 Community examples from TensorFlow and Keras
• ❤️ Open-source spirit of education and exploration

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📬 Contact

For questions, collaborations, or feedback: 📧 [email protected]

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"Learning never exhausts the mind." — Leonardo da Vinci