Deep-Learning 🎓

A collection of various deep learning architectures, models, and tips.

Contains PyTorch Notebooks on Computer Vision. (NLP soon)

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Latest✨ Projects: Cats Vs Dogs - ResNet18 (Let's see, what will model choose?)

Cats vs Dogs (Acc: ~95 %) 🐱 🆚 🐶

Using Kaggle Dataset and ImageNet stats for normalizing the data, the model acheived an accuracy of ~95% of Accuracy. 🙌

- In joint-efforts 🤝 with San Sequeira ✌

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Regressors 📈

→ Wine Quality Predictor 🥂

Computer Vision 🧠

→ Multi-Label Protein Classifier 🦠🧫

→ Facial Expression Recognizer 😅😓😱😩🥴

→ CIFAR - 10 [✈, 🚗, 🦅, 🐱, 🦌, 🐶, 🐸, 🐎, 🚢, 🚚]

→ MNIST - (val_acc = 97.81) ✍

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Contributing 🤝

Please read CONTRIBUTING.md for details on the code of conduct, and the process for submitting pull requests to us.

Acknowledgements 👏

• Kaggle
• PyTorch Docs
• Jovian.ml

License ⚠️

This collection of models is licensed under the MIT License - see the LICENSE.md file for details

Feel free to use and modify/customize the models.
If any issues, open an issues I'll try to fix it as soon as possible.🙂