Phung Huynh Xuan's repositories
Pytorch-DPPO
Pytorch implementation of Distributed Proximal Policy Optimization: https://arxiv.org/abs/1707.02286
Deep-Learning-Nanodegree
Projetos e aulas do treinamento da Udacity sobre Deep Learning
pix2pix
Image-to-image translation with conditional adversarial nets
DenseNet
Densely Connected Convolutional Networks, In CVPR 2017 (Best Paper Award).
gym-http-api
API to access OpenAI Gym from other languages via HTTP
NIT-ICCV17Challenge
This code submission for the ICCV 17 Real Versus Fake Expressed Emotion Challenge provides source code to extract the features and classifiy each video for a given folder.
gcForest
This is the official implementation for the paper 'Deep forest: Towards an alternative to deep neural networks'
ensemble-adv-training
Ensemble Adversarial Training on MNIST
DSB2017
The solution of team 'grt123' in DSB2017
quadrocopter-deep-q-learning-tensorflow
Quadrotor simulator mainly purposed to train neural network to control quadrotor flight via deep q learning algorithm
Atari
Persistent advantage learning dueling double DQN for the Arcade Learning Environment
trajopt
Trajectory Optimization
canton
yet another DL framework
mask_rcnn_pytorch
Mask RCNN in PyTorch
lab
A customisable 3D platform for agent-based AI research
fastText
Library for fast text representation and classification.
awesome-deep-learning-papers
The most cited deep learning papers
tsne
A python wrapper for Barnes-Hut tsne
deep-makeover
Deep learning project to transform male portraits into female and vice versa
AirSim
Open source simulator based on Unreal Engine for autonomous vehicles from Microsoft AI & Research
neural-style
Torch implementation of neural style algorithm
flight
Flight code for MIT CSAIL Robot Locomotion Group flying-through-forests project
pytorch-generative-adversarial-networks
A very simple generative adversarial network (GAN) in PyTorch
DeepTraffic-solution
My solution to dieeptraffic contest
firmware
This repository contains pre-compiled binaries of the current Raspberry Pi kernel and modules, userspace libraries, and bootloader/GPU firmware.
how-to-code-samples
These applications are code sample exercises using the Intel IoT Developer Kit, Intel Edison board or Intel IoT Gateway, sensors, actuators, cloud platforms, and APIs.