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Deep_reinforcement_learning_Course
Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorchawesome-ai-tools-for-game-dev
A curated list of awesome AI tools for game developersCatDCGAN
A DCGAN that generate Cat pictures π±βπ»unity_ml_agents_course
Learn to create agents in Unity ML using Deep Reinforcement Learning with Tensorflow.the_mayan_adventure
The Mayan Adventure is an open-source reinforcement learning environment for Unity ML-Agents. In this environment, you train your agent (Indie) to find the golden statue in this dangerous environment full of traps.jammo_the_robot
Time-series-prediction-and-text-generation
Built RNNs that can generate sequences based on input data - with a focus on two applications: used real market data in order to predict future Apple stock prices using an RNN model. The second one will be trained on Sir Arthur Conan Doyle's classic novel Sherlock Holmes and generates wacky sentences based on it that may - or may not - become the next great Sherlock Holmes novel.AI_NANODEGREE-Computer-Vision-Capstone-Project-Facial-Keypoint-Detection-and-Real-time-Filtering
A CNN that make facial keypoint detection and real time filteringml-agents-snowball-fight
A multi-agent environment using Unity ML-Agents ToolkitBaselines_icm
[Work in Progress] ICM plugin for OpenAI BaselinesMLAgents-Tanks
A multi-agent environment using Unity ML-Agents Toolkit where two agents compete in a 1vs1 tank fight gamecifar-10-classifier-pytorch
A simple cifar-10 classifier with PyTorchDog-Breed-Classifier
Convolutional Neural Network that recognizes dog breedsNews-Crawler-Parse-Backend
This is a crawler made with Scrapy.py to crawl french news articles and send them in your Parse.com backendTest_doom_dqn
deep_q_learning
A DQL implementation with kerassquad_v1.1_for_question_generation
GAN_implementations
Some GAN (Generative Adversarial Networks) implementations to generate MNIST imagesPolicy_gradients_CartPole
A Policy Gradient Learning with CartPole-v0 for Siraj Raval's challengeDLFN_Face_generator
A Generative Adversarial Network that generate human facesNewsSwipe
This version is for educational purposes only. The goal is to learn to create android applications with network. I create this app with the help of the excellent course : Udacity Android basics : networkingNewsSwipeWebsiteV1.0
First version of NewsSwipe website december 2015deep-rl-course
DLFN_Language_Translation
A Seq-to-seq model that translates English to FrenchLittleCoffeeShop
Little Coffee Shop is an app that helps you to order in a coffee shop called "Little Coffee Shop".DLFN_Generate_The_Simpsons_Scripts
An RNN that generates new scripts for the Simpsons' tv showSimpleLinearRegression_PhiladelphiaRateCrime_HousePricePrediction
This Machine Learning model helps us to predict of houses prices in Philadelphia region based on crime rate.Where_is_my_cat
A series of YOLO experimentations using DarkFlowDGAN-Implementations
Some DGAN (Deep Generative Adversarial Networks) implementations to generate human facesPlayBot_BackEnd
Playbot API in NodeJSPlayBot_website
Website for a fake project : Playbot a bot that proposes games based on user tastessonictest
simonini_thomas_website
First version on my personal websitesimonini_thomas_website_new
My new personal websiteNihongoHanasuKotoGaDekimasu
Android app to learn some basic Japanese vocabulary. Created with the help of the Udacity's course : Android basics, Multiscreen appsDLFN_First_neural_network
Deep Learning Foundations Nanodegree. Udacity Project 1: Your first neural networkPolicy-Gradient-Doom--
Policy Gradient based agent that tries to survive in an hostile environement by collecting health.tanks
StockHawk
An Android App that displays stock pricesNewsAppRV
NewsAppRV is a demo app made as an example for the article RecyclerView made easy.PlayBot
PlayBot is an AI that propose games based on user tastesMoviesHuntNano
Movies Hunt is an Android app that helps user to find the next movie to watch.DLFN_Image_classification
In this project, you'll classify images from the CIFAR-10 dataset. The dataset consists of airplanes, dogs, cats, and other objects. The dataset will need to be preprocessed, then train a convolutional neural network on all the samples. You'll normalize the images, one-hot encode the labels, build a convolutional layer, max pool layer, and fully connected layer. At then end, you'll see their predictions on the sample images.Love Open Source and this site? Check out how you can help us