Deeplearning4j Alternatives

This may not be the best deep learning framework, but it is a deep learning framework.

TensorFlow is an open source software library for machine learning in various kinds of perceptual and language understanding tasks. It was originally developed by the Google and later released under the Apache 2.0 open source license on Nov 9, 2015.

PyTorch enables fast, flexible experimentation and efficient production through a hybrid front-end, distributed training, and ecosystem of tools and libraries.

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.

A tiny Autograd engine (with a bite! :)). Implements backpropagation (reverse-mode autodiff) over a dynamically built DAG and a small neural networks library on top of it with a PyTorch-like API. Both are tiny, with about 100 and 50 lines of code respectively.

PyCaret is an open source low-code machine learning library in Python that aims to reduce the hypothesis to insights cycle time in a ML experiment. It enables data scientists to perform end-to-end experiments quickly and efficiently.

mlpack is a 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.

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.

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.

CatBoost is an open-source gradient boosting on decision trees library with categorical features support out of the box for Python, R.

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...