{"id":21681713,"url":"https://github.com/jersongb22/generativedeeplearning","last_synced_at":"2026-04-19T04:38:02.992Z","repository":{"id":231940517,"uuid":"783075336","full_name":"JersonGB22/GenerativeDeepLearning","owner":"JersonGB22","description":"Links to my works, where a variety of generative models are implemented using TensorFlow and PyTorch. 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These models can generate a variety of data, including images, sounds, text, and more.\n\n## **Implemented Models**\n\nThe following are the generative deep learning models that I have implemented to date:\n\n1. [**Autoencoder**](https://github.com/JersonGB22/Autoencoder-TensorFlow-PyTorch): An autoencoder is a neural network that is trained to copy its input to its output. It is used to learn efficient representations of the input data and/or to reduce the dimension of the input data. \n\n2. [**Conditional GAN**](https://github.com/JersonGB22/ConditionalGAN-TensorFlow-PyTorch): An extension of GANs that allows generating data conditioned on certain information. \n\n3. [**CycleGAN**](https://github.com/JersonGB22/CycleGAN-TensorFlow-PyTorch): A model for translating images from one domain to another, without the need for paired data. \n\n4. [**DCGAN (Deep Convolutional Generative Adversarial Networks)**](https://github.com/JersonGB22/DCGAN-TensorFlow-PyTorch): A variant of GANs that uses convolutional layers in its networks. \n\n5. [**GAN (Generative Adversarial Networks)**](https://github.com/JersonGB22/GAN-TensorFlow-PyTorch): GANs are a type of generative model that uses two neural networks, a generator and a discriminator, which are trained simultaneously. \n\n6. [**GAN Controllable Generation**](https://github.com/JersonGB22/GANControllableGeneration-TensorFlow-PyTorch): A model that allows controlling the characteristics of the generated data. \n\n7. [**Neural Style Transfer**](https://github.com/JersonGB22/NeuralStyleTransfer-TensorFlow-PyTorch): A model that applies the style of one image to another. \n\n8. [**Pix2Pix**](https://github.com/JersonGB22/Pix2Pix-TensorFlow-PyTorch): A model for translating images from one domain to another. \n\n9. [**VAE (Variational Autoencoder)**](https://github.com/JersonGB22/VAE-TensorFlow-PyTorch): A type of autoencoder that produces a distribution of the input data rather than a single representation. \n\n## **Contributions**\n\nContributions to this repository are welcome. If you have any questions or suggestions, please do not hesitate to contact me.\n\n## **Technological Stack**\n[![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge\u0026logo=python\u0026logoColor=white\u0026labelColor=101010)](https://docs.python.org/3/) \n[![TensorFlow](https://img.shields.io/badge/TensorFlow-FF6F00?style=for-the-badge\u0026logo=tensorflow\u0026logoColor=white\u0026labelColor=101010)](https://www.tensorflow.org/api_docs)\n[![PyTorch](https://img.shields.io/badge/PyTorch-EE4C2C?style=for-the-badge\u0026logo=pytorch\u0026logoColor=white\u0026labelColor=101010)](https://pytorch.org/docs/stable/index.html)\n[![Plotly](https://img.shields.io/badge/Plotly-3F4F75?style=for-the-badge\u0026logo=plotly\u0026logoColor=white\u0026labelColor=101010)](https://plotly.com/)\n\n## **Contact**\n[![Gmail](https://img.shields.io/badge/Gmail-D14836?style=for-the-badge\u0026logo=gmail\u0026logoColor=white\u0026labelColor=101010)](mailto:jerson.gimenesbeltran@gmail.com)\n[![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white\u0026labelColor=101010)](https://www.linkedin.com/in/jerson-gimenes-beltran/)\n[![GitHub](https://img.shields.io/badge/GitHub-181717?style=for-the-badge\u0026logo=github\u0026logoColor=white\u0026labelColor=101010)](https://github.com/JersonGB22/)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjersongb22%2Fgenerativedeeplearning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjersongb22%2Fgenerativedeeplearning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjersongb22%2Fgenerativedeeplearning/lists"}