amrakibulhasan Goto Github PK
Name: A M Rakibul Hasan
Type: User
Twitter: iamashek
Location: Chattogram
Name: A M Rakibul Hasan
Type: User
Twitter: iamashek
Location: Chattogram
Solutions to DeepLearning.AI Generative Adversarial Networks (GANs) Specialization
Generative Adversarial Networks (GANs) with TensorFlow 2.0, Published by Packt
Implementation and explanation of GAN's in Keras. A toy example of generating handwritten digits using the MNIST dataset to gain some intuition into how Generators and Discriminators work.
This repository contains code and slides for the presentation I gave at Atlanta Deep Learning meet-up. We coded the GAN with participants after the presentation.
A Hybrid Adversarial Generative Approach to Generate a White Pegasus
This is the collection of my solutions to the assignments of the course "Generative Deep Learning with TensorFlow" offered by Deeplearning.ai through Coursera.
Repository for implementation of generative models with Tensorflow 1.x
Generate dogs images using DCGAN in PyTorch. https://medium.com/@shwetagoyal41/gans-a-brief-introduction-to-generative-adversarial-networks-f06216c7200e
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Annotated, understandable, and visually interpretable PyTorch implementations of: VAE, BIRVAE, NSGAN, MMGAN, WGAN, WGANGP, LSGAN, DRAGAN, BEGAN, RaGAN, InfoGAN, fGAN, FisherGAN
Implementation of various Generative Models in Pytorch
PyTorch implemented generative models for CelebA dataset: DCGAN, LSGAN, WGAN, WGANGP, InfoGAN, BEGAN, VAE, VAEGAN
Generate models implementions , including GAN and Auto Encoder. Based on pytorch 1.0+
Implements a GAN in PyTorch
A small overview of what GANs and their main variants are, with related implementations.
(PyTorch) Implementations of GAN, Improved GAN, DCGAN, LAPGAN, and InfoGAN in PyTorch
This is a GACNN that is built completely from scratch. It only uses numpy. It is demonstrated in the code using the mnist dataset.
Generative Deep Learning Models in Pytorch
Pytorch implementations of generative models: VQVAE2, AIR, DRAW, InfoGAN, DCGAN, SSVAE
Deep Convolutional Generative Adversarial Network is implemented to generate handwritten digits. Generator generates artificial samples and Discriminator distinguishes real and artificial samples
Implementing GAN for two different datasets with PyTorch.
Pytorch implementation of Generative Models
Topic Modelling for Humans
Build and train state-of-the-art natural language processing models using BERT
Google Research
Master Deep Learning Algorithms with Extensive Math by Implementing them using TensorFlow
Hands-On Generative Adversarial Networks with PyTorch 1.x, Published by Packt
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.