paulcarfantan's repositories
deploying-machine-learning-models
Example Repo for the Udemy Course "Deployment of Machine Learning Models"
covid-xprize
Open-source repository containing examples and documentation for the Cognizant XPRIZE Pandemic Response Challenge
github-slideshow
A robot powered training repository :robot:
MLclass
My main Machine Learning class
baselines
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
Pytorch-RL-Agents
Our implementations of a few RL algo with Pytorch
RL-Adventure
Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL
Number-Theory-Python
Python code to implement various number theory, elliptic curve and finite field computations.
neural-style
Neural style in TensorFlow! :art:
BEGAN-tensorflow
Tensorflow implementation of "BEGAN: Boundary Equilibrium Generative Adversarial Networks"
models
Models built with TensorFlow
fast-style-transfer
TensorFlow CNN for fast style transfer! ⚡🖥🎨🖼
pix2pix-tensorflow
Tensorflow port of Image-to-Image Translation with Conditional Adversarial Nets https://phillipi.github.io/pix2pix/
mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
tensorflow
Computation using data flow graphs for scalable machine learning
CycleGAN
Software that can generate photos from paintings, turn horses into zebras, perform style transfer, and more.
pix2pix
Image-to-image translation with conditional adversarial nets
generative-models
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
deep-photo-styletransfer
Code and data for paper "Deep Photo Style Transfer": https://arxiv.org/abs/1703.07511
tensorflow-fast-neuralstyle
Tensorflow implementation of "Perceptual Losses for Real-Time Style Transfer and Super-Resolution."
mxnet-gan
MultiGPU enabled image generative models (GAN and DCGAN)
ganhacks
starter from "How to Train a GAN?" at NIPS2016