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https://github.com/echosingh/ml-deeplearning-projects

A curated collection of machine learning projects focused on foundational and advanced deep learning models, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and GoogLeNet. Perfect for students and enthusiasts exploring the core principles of ML and DL.
https://github.com/echosingh/ml-deeplearning-projects

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A curated collection of machine learning projects focused on foundational and advanced deep learning models, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and GoogLeNet. Perfect for students and enthusiasts exploring the core principles of ML and DL.

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README

          

# 🧠 ML Deep Learning Projects

This repository contains a collection of deep learning projects developed during my 6th semester coursework. These projects explore a range of advanced topics in computer vision and generative modeling using popular architectures like **CNNs**, **GANs**, **WGAN-GP**, and **GoogLeNet**.

All notebooks were developed and tested using **Python (Jupyter Notebooks)** and are also available on my Kaggle profile:
🔗 [https://www.kaggle.com/adi2606](https://www.kaggle.com/adi2606)

## 📁 Project Files

| File Name | Description |
|----------|-------------|
| `colorlandscape-gan.ipynb` | GAN model trained to generate realistic landscape images from noise. |
| `deepfake-detection-images-videos.ipynb` | A project using CNNs to detect deepfakes in both images and video frames. |
| `face-aging-using-conditional-gan.ipynb` | Conditional GAN that simulates age progression on facial images. |
| `sign-language-recognition-with-cnn.ipynb` | CNN-based model for recognizing Sign Language gestures. |
| `wgan-gp-mnist-the-ultimate-pixel-showdown.ipynb` | Implementation of WGAN-GP on MNIST for stable training and better digit generation. |

## 🔒 License

This repository is licensed under the terms of the [MIT License](LICENSE).

## 🙌 Acknowledgements

These projects were created as part of the academic curriculum in my 6th semester. They serve both as educational exercises and as demonstrations of practical deep learning applications.

If you find this work useful, consider visiting my [Kaggle profile](https://www.kaggle.com/adi2606) for more notebooks and competitions.

Feel free to fork, star , or contribute!