fast, flexible C++ machine learning library

mlpack is an intuitive, fast, and flexible C++ machine learning library with bindings to other languages. It is meant to be a machine learning analog to LAPACK, and aims to implement a wide array of machine learning methods and functions as a "swiss army knife" for machine learning researchers. In addition to its powerful C++ interface, mlpack also provides command-line programs and Python bindings.

lightbulb_outline View ideas list

Technologies

  • c++

Topics

comment IRC Channel
email Mailing list
mail_outline Contact email

mlpack 2019 Projects

  • Roberto Hueso Gomez
    Advanced Kernel Density Estimation Improvements
    Kernel Density Estimation (KDE) is a widely used non-parametric technique to estimate a probability density function. mlpack already had an...
  • Mehul Kumar Nirala
    Application of ANN Algorithms Implemented in mlpack
    Application of ANN algorithms implemented in mlpack. Implementation of VGG 19 for Image Classification. LSTM Networks for Sentiment Analysis. LSTM...
  • Saksham Bansal
    Implementing Essential Deep Learning Modules
    In recent years, generative adversarial networks have proven to be very effective for training generative models and hundreds of different variants...
  • Toshal Agrawal
    Implementing Essential Deep Learning Modules
    Various Deep learning architectures such as deep neural networks and recurrent neural networks are applied to fields including computer vision,...
  • Sreenik Seal
    mlpack-Tensorflow Translator
    The purpose of this project is to build a converter that can translate models built in Tensorflow, Keras, Pytorch, Mxnet, ONNX to mlpack's model...
  • Rahul Ganesh Prabhu
    NeuroEvolution of Augmenting Topologies & Multi-Objective Optimization
    NeuroEvolution of Augmenting Toplogies (NEAT) is a genetic algorithm that can evolve networks of unbound complexity by starting from simple networks...
  • xiaohong ji
    PPO(Proximal Policy Optimization)
    A new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment,...
  • Sangyeon Kim
    Quantum Gaussian Mixture Models
    Gaussian Mixture Model (GMM) is widely used in computer vision as a state-of-the-art clustering algorithm. This project proposes Quantum Gaussian...
  • Jeffin Sam
    String Processing Utilities
    To develop String Processing Utilities for boosting mlpack library to manipulate string data types and to convert it into numeric datatype to apply...
close

2019