Displaying 1 to 20 from 51 results

mind - A neural network library built in JavaScript

  •    Javascript

A flexible neural network library for Node.js and the browser. Check out a live demo of a movie recommendation engine built with Mind. Use plugins created by the Mind community to configure pre-trained networks that can go straight to making predictions.

ImageAI - A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities

  •    Python

A python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code. Built with simplicity in mind, ImageAI supports a list of state-of-the-art Machine Learning algorithms for image prediction, custom image prediction, object detection, video detection, video object tracking and image predictions trainings. ImageAI currently supports image prediction and training using 4 different Machine Learning algorithms trained on the ImageNet-1000 dataset. ImageAI also supports object detection, video detection and object tracking using RetinaNet, YOLOv3 and TinyYOLOv3 trained on COCO dataset. Eventually, ImageAI will provide support for a wider and more specialized aspects of Computer Vision including and not limited to image recognition in special environments and special fields.

premonish - Predict which DOM element a user will interact with next.

  •    Javascript

Predict which DOM element a user will interact with next. You give it a list of elements and it will try to predict when a user is about to mouse over one of those elements.

PredictionIO - Machine Learning Server

  •    Scala

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery. It helps to predict user behaviors.

android-yolo - Real-time object detection on Android using the YOLO network with TensorFlow

  •    C++

android-yolo is the first implementation of YOLO for TensorFlow on an Android device. It is compatible with Android Studio and usable out of the box. It can detect the 20 classes of objects in the Pascal VOC dataset: aeroplane, bicycle, bird, boat, bottle, bus, car, cat, chair, cow, dining table, dog, horse, motorbike, person, potted plant, sheep, sofa, train and tv/monitor. The network only outputs one predicted bounding box at a time for now. The code can and will be extended in the future to output several predictions. To use this demo first clone the repository. Download the TensorFlow YOLO model and put it in android-yolo/app/src/main/assets. Then open the project on Android Studio. Once the project is open you can run the project on your Android device using the Run 'app' command and selecting your device.

frugally-deep - Header-only library for using Keras models in C++.

  •    C++

Would you like to build/train a model using Keras/Python? And would you like run the prediction (forward pass) on your model in C++ without linking your application against TensorFlow? Then frugally-deep is exactly for you. Layer types typically used in image recognition/generation are supported, making many popular model architectures possible (see Performance section).

tensorflow-lstm-sin - TensorFlow 1.3 experiment with LSTM (and GRU) RNNs for sine prediction

  •    Python

Single- and multilayer LSTM networks with no additional output nonlinearity based on aymericdamien's TensorFlow examples and Sequence prediction using recurrent neural networks. Experiments with varying numbers of hidden units, LSTM cells and techniques like gradient clipping were conducted using static_rnn and dynamic_rnn. All networks have been optimized using Adam on the MSE loss function.

Wharton_Stat_422_722 - The official class webpage for Statistics 422/722 taught at Wharton in the Spring of 2017

  •    TeX

This is the course homepage for STAT 422/722 for the Spring semester 2017 at The Wharton School of the University of Pennsylvania taught by Professor Adam Kapelner. The syllabus can be found here. Audio for lectures should be on canvas except for the first lecture (links below).

graph-pattern-learner - Evolutionary Graph Pattern Learner that learns SPARQL queries for a given set of source-target-pairs from an endpoint

  •    Python

In this repository you find the code for a graph pattern learner. Given a list of source-target-pairs and a SPARQL endpoint, it will try to learn SPARQL patterns. Given a source, the learned patterns will try to lead you to the right target. As you can immediately see, associations don't only follow a single pattern. Our algorithm is designed to be able to deal with this. It will try to learn several patterns, which in combination model your input list of source-target-pairs. If your list of source-target-pairs is less complicated, the algorithm will happily terminate earlier.

gridpp - Gridded post-processor

  •    C++

The program post-processes NetCDF files used at MET-Norway by using various downscaling and calibration methods. Post-processed forecasts are placed in a second Netcdf file, which has the desired output grid.

infer - 🔮 Use TensorFlow models in Go to evaluate Images (and more soon!)

  •    Go

Infer is a Go package for running predicitions in TensorFlow models. This package provides abstractions for running inferences in TensorFlow models for common types. At the moment it only has methods for images, however in the future it can certainly support more.

cryptosite - Library for prediction of cryptic binding sites

  •    Python

CryptoSite is a computational tool for predicting the location of cryptic binding sites in proteins and protein complexes. A web interface is also available.

stock-forecast - Simple stock & cryptocurrency price forecasting console application, using PHP Machine Learning library (https://github

  •    PHP

This is a very simple initial version of the app. At this moment it only can use some linear algorithms, SquareLevels, Support Vector Regression and a basic linear regression based on Cumulative Moving Averages.

open-data-bikes-analysis - Open Data Bikes Sharing Stations Analysis

  •    Jupyter

Analyze bikes sharing station data from Bordeaux and Lyon Open Data (French cities). Use the Python 3 programming language in Jupyter notebooks and the following libraries: pandas, numpy, seaborn, matplotlib, scikit-learn, xgboost.

node-google-prediction - A node.js client for the Google Prediction API

  •    Javascript

A node.js client for the Google Prediction API - To be used for Server to Server applications. This is a node.js client library that abstracts the Google Prediction API integration complexities, and allows you to get up and running quickly and start using the api to your business benefit.

FTRLProximal - R package for online training of regression models using FTRL Proximal

  •    R

This is an R package of the FTRL Proximal algorithm for online learning of elastic net logistic regression models. For more info on the algorithm please see Ad Click Prediction: a View from the Trenches by McMahan et al. (2013).

Gambetta_NetworkedDemo - Fast-Paced Multiplayer: Sample Code and Live Demo - Gabriel Gambetta's Multiplayer Network Demo in Unity C# as Networked using Lidgren Network

  •    CSharp

Unity Demo showcasing networking concepts including prediction, interpolation and reconciliation in a networked (multiplayer) environment. This is a minimal demo project made in Unity 2017.2.0f3 (but it should work in older versions as well). The demo project is a very close implementation of the "Gambetta Demo" on Network Architecture - All credits to Gabriel Gambetta for that.

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