mlpack is described as 'C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features' and is an app. There are eight alternatives to mlpack for a variety of platforms, including Linux, Mac, Windows, Web-based and Python apps. The best mlpack alternative is TensorFlow, which is both free and Open Source. Other great apps like mlpack are TFLearn, Keras, Training Mule and Darknet.
TensorFlow is an open source software library for machine learning in various kinds of perceptual and language understanding tasks.
TensorFlow is the most popular Windows, Mac & Linux alternative to mlpack.
TensorFlow is the most popular Open Source & free alternative to mlpack.
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it.
Keras is a deep learning API written in Python and capable of running on top of either JAX, TensorFlow, or PyTorch.
Keras is the most popular Self-Hosted alternative to mlpack.
Training Mule allows you or your team to easily label images, providing you with the datasets that you require for the best results. With your images labelled, we will train the network of your choice and provide you with the model ready for use.
Training Mule is the most popular Web-based alternative to mlpack.
Darknet: Open Source Neural Networks in C Darknet is an open source neural network framework written in C and CUDA. It is fast, easy to install, and supports CPU and GPU computation.
Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs.
MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both...
Cloud AutoML helps you easily train high quality custom machine learning models with limited machine learning expertise needed.
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