Displaying 1 to 20 from 36 results

limdu - Machine-learning for Node.js

  •    Javascript

Limdu is a machine-learning framework for Node.js. It supports multi-label classification, online learning, and real-time classification. Therefore, it is especially suited for natural language understanding in dialog systems and chat-bots.Limdu is in an "alpha" state - some parts are working (see this readme), but some parts are missing or not tested. Contributions are welcome.

tensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook

  •    Jupyter

This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

vehicle-detection - Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree

  •    Jupyter

Vehicle detection using these machine learning and computer vision techniques. First, you need to get training data(cars and not-cars). You can get car images from GTI vehicle image database, KITTI vision benchmark). And over 1500 images per each is good for this project.

talon - Mailgun library to extract message quotations and signatures

  •    Python

Mailgun library to extract message quotations and signatures.For machine learning talon currently uses the scikit-learn library to build SVM classifiers. The core of machine learning algorithm lays in talon.signature.learning package. It defines a set of features to apply to a message (featurespace.py), how data sets are built (dataset.py), classifier’s interface (classifier.py).

svmjs - Support Vector Machine in Javascript (SMO algorithm, supports arbitrary kernels) + GUI demo

  •    Javascript

svmjs is a lightweight implementation of the SMO algorithm to train a binary Support Vector Machine. As this uses the dual formulation, it also supports arbitrary kernels. Correctness test, together with MATLAB reference code are in /test. Corresponding code is inside /demo directory.

Hyperparameter-Optimization-of-Machine-Learning-Algorithms - Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)

  •    Jupyter

This code provides a hyper-parameter optimization implementation for machine learning algorithms, as described in the paper: L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.061. To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model's performance. In this paper, optimizing the hyper-parameters of common machine learning models is studied. We introduce several state-of-the-art optimization techniques and discuss how to apply them to machine learning algorithms. Many available libraries and frameworks developed for hyper-parameter optimization problems are provided, and some open challenges of hyper-parameter optimization research are also discussed in this paper. Moreover, experiments are conducted on benchmark datasets to compare the performance of different optimization methods and provide practical examples of hyper-parameter optimization.

JSAT - Java Statistical Analysis Tool, a Java library for Machine Learning

  •    Java

JSAT is a library for quickly getting started with Machine Learning problems. It is developed in my free time, and made available for use under the GPL 3. Part of the library is for self education, as such - all code is self contained. JSAT has no external dependencies, and is pure Java. I also aim to make the library suitably fast for small to medium size problems. As such, much of the code supports parallel execution.If you want to use the bleeding edge, but don't want to bother building yourself, I recomend you look at jitpack.io. It can build a POM repo for you for any specific commit version. Click on "Commits" in the link and then click "get it" for the commit version you want.

ThunderSVM - A Fast SVM Library on GPUs and CPUs

  •    C++

The mission of ThunderSVM is to help users easily and efficiently apply SVMs to solve problems. ThunderSVM exploits GPUs and multi-core CPUs to achieve high efficiency. It supports all functionalities of LibSVM such as one-class SVMs, SVC, SVR and probabilistic SVMs. It can use same command line options as LibSVM. It supports Python, R and Matlab interfaces.

rb-libsvm - Ruby language bindings for LIBSVM

  •    C++

This package provides a Ruby bindings to the LIBSVM library. SVM is a machine learning and classification algorithm, and LIBSVM is a popular free implementation of it, written by Chih-Chung Chang and Chih-Jen Lin, of National Taiwan University, Taipei. See the book "Programming Collective Intelligence," among others, for a usage example. There is a JRuby implementation of this gem named jrb-libsvm by Andreas Eger.

node-svm - Support Vector Machines for nodejs

  •    Javascript

Support Vector Machine (SVM) library for nodejs & io.js . Support vector machines are supervised learning models that analyze data and recognize patterns. A special property is that they simultaneously minimize the empirical classification error and maximize the geometric margin; hence they are also known as maximum margin classifiers.

golinear - liblinear bindings for Go

  •    Go

golinear is a package for training and using linear classifiers in the Go programming language (golang).Ubuntu and Debian provide packages for liblinear. However, at the time of writing (July 2, 2014), these were serverly outdated. This package requires version 1.9 or later.

lineargo - LinearGo (Go wrapper for LIBLINEAR): A Library for Large Linear Classification

  •    Go

This is a Golang wrapper for LIBLINEAR (C.-J. Lin et al.) (GitHub). Note that the interface of this package might be slightly different from liblinear C interface because of Go convention. Yet, I'll try to align the function name and functionality to liblinear C library.GoDoc: 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.

Algorithm-LibLinear - A Perl binding for LIBLINEAR, a library for classification/regression using linear SVM and logistic regression

  •    Perl

Algorithm::LibLinear - A Perl binding for LIBLINEAR, a library for classification/regression using linear SVM and logistic regression. Algorithm::LibLinear is an XS module that provides features of LIBLINEAR, a fast C library for classification and regression.

SimpleSvm - A minimalistic educational hypervisor for Windows on AMD processors.

  •    C++

SimpleSvm is a minimalistic educational hypervisor for Windows on AMD processors. It aims to provide small and explanational code to use Secure Virtual Machine (SVM), the AMD version of Intel VT-x, with Nested Page Tables (NPT) from a windows driver. SimpleSvm is inspired by SimpleVisor, an Intel x64/EM64T VT-x specific hypervisor for Windows, written by Alex Ionescu (@aionescu).

SimpleSvmHook - SimpleSvmHook is a research purpose hypervisor for Windows on AMD processors.

  •    C++

SimpleSvmHook is a research purpose hypervisor for Windows on AMD processors. It hooks kernel mode functions and protects them from being detected using Nested Page Tables (NPT), part of AMD Virtualization (AMD-V) technology. This project is meant to serve as an example implementation of virtual machine introspection (VMI) on AMD processors and highlight differences from similar VMI implementations on Intel processors.

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