Displaying 1 to 15 from 15 results

lightning-bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers.

  •    Python

The main goal of Bolts is to enable rapid model idea iteration. Great! We have LinearRegression and LogisticRegression implementations with numpy and sklearn bridges for datasets! But our implementations work on multiple GPUs, TPUs and scale dramatically...

Personae - 📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading

  •    Python

Personae is a repo that implements papers proposed methods in Deep Reinforcement Learning & Supervised Learning and applies them to Financial Market. It will start from 2018-08-24 to 2018-09-01 a timestamp that I successfully found a job.

StockPricePrediction - Stock Price Prediction using Machine Learning Techniques

  •    Jupyter

To examine a number of different forecasting techniques to predict future stock returns based on past returns and numerical news indicators to construct a portfolio of multiple stocks in order to diversify the risk. We do this by applying supervised learning methods for stock price forecasting by interpreting the seemingly chaotic market data. Download the Dataset needed for running the code from here.

YCML - A Machine Learning and Optimization framework for Objective-C and Swift (MacOS and iOS)

  •    Objective-C

YCML is an Artificial Intelligence, Machine Learning and Optimization framework written in Objective-C. YCML can be used both in Objective-C as well as in Swift. YCML has been verified to run on MacOS and iOS. Above all, YCML attempts to bring high-quality published algorithms to Swift/Objective-C, using optimized implementations. Referenced papers for the implementation of each algorithm are available at the end of this document.

fashion - The Fashion-MNIST dataset and machine learning models.

  •    R

Training AI machine learning models on the Fashion MNIST dataset. Fashion-MNIST is a dataset consisting of 70,000 images (60k training and 10k test) of clothing objects, such as shirts, pants, shoes, and more. Each example is a 28x28 grayscale image, associated with a label from 10 classes. The 10 classes are listed below.

sutton-barto-rl-exercises - Learning reinforcement learning by implementing the algorithms from reinforcement learning an introduction

  •    Jupyter

Pull requests and bug report are welcome. Note: if you find the formatting some notebooks (esp. with many equations) doesn't look good on github,, try visualize them on http://nbviewer.jupyter.org/github/zyxue/sutton-barto-rl-exercises/tree/master/.

tetrisRL - A Tetris environment to train machine learning agents

  •    Python

You need to have pytorch pre-installed. Easy to use download scripts can be found on their website. The interface is similar to an OpenAI Gym environment.


  •    CSharp

NeuralNetwork.NET is a .NET Standard 2.0 library that implements sequential and computation graph neural networks with customizable layers, built from scratch with C#. It provides simple APIs designed for quick prototyping to define and train models using stochastic gradient descent, as well as methods to save/load a network model and its metadata and more. The library also exposes CUDA-accelerated layers with more advanced features that leverage the GPU and the cuDNN toolkit to greatly increase the performances when training or using a neural network.

Laurae - Advanced High Performance Data Science Toolbox for R by Laurae

  •    R

04/03/2017: Added Deep Forest implementation in R using xgboost, which may provide similar performance versus very simple Convolutional Neural Networks (CNNs), and slightly better results than boosted models. You can find the paper here. Supported: Complete-Random Tree Forest, Cascade Forest, Multi-Grained Scanning, Deep Forest. You can use Gradient Boosting to get a sort of "Deep Boosting" model. 10/02/2017: Added Partial Dependence Analysis, currently a skeleton but I will build more on it. It is fully working for the analysis of single observations against an amount of features you specify. The multiple observation version is not yet working when it comes to analyzing statistically the results.

tensorflow-101 - TensorFlow 101: Introduction to TensorFlow

  •    Jupyter

In this repository, source codes will be shared while capturing "TensorFlow 101: Introduction to Deep Learning" online course published on Udemy. The course consists of 18 lectures and includes 3 hours material.

WannaPark - Project aimed at presenting a model to find a vacant parking spot in real time and ensure car safety using Deep Learning (Parking spot Classification and Face recognition)

  •    Python

A Real-time car parking system model using Deep learning applied on CCTV camera images, developed for the competition IdeaQuest, held among the summer interns of Qualcomm. We also propose a novel method for internal navigation and prevention of Car thefts (all details are not released yet).

cattonum - Encode Categorical Features

  •    R

The goal of cattonum is to be a one-stop shop for all categorical encoding needs. The development version of cattonum can be installed from GitHub.

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