Torch vs ScalaNLP

Overview

Torch

Stacks355

Followers61

Votes0

GitHub Stars9.1K

Forks2.4K

ScalaNLP

Stacks2

Followers12

Votes0

GitHub Stars3.5K

Forks694

ScalaNLP vs Torch: What are the differences?

What is ScalaNLP? A suite of machine learning and numerical computing libraries. ScalaNLP is a suite of machine learning and numerical computing libraries.

What is Torch? An open-source machine learning library and a script language based on the Lua programming language. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

ScalaNLP and Torch can be categorized as "Machine Learning" tools.

Some of the features offered by ScalaNLP are:

ScalaNLP is the umbrella project for several libraries:
Breeze is a set of libraries for machine learning and numerical computing
Epic is a high-performance statistical parser and structured prediction library

On the other hand, Torch provides the following key features:

A powerful N-dimensional array
Lots of routines for indexing, slicing, transposing
Amazing interface to C, via LuaJIT

ScalaNLP is an open source tool with 2.95K GitHub stars and 678 GitHub forks. Here's a link to ScalaNLP's open source repository on GitHub.

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Detailed Comparison

Torch	ScalaNLP
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.	ScalaNLP is a suite of machine learning and numerical computing libraries.
A powerful N-dimensional array; Lots of routines for indexing, slicing, transposing; Amazing interface to C, via LuaJIT; Linear algebra routines; Neural network, and energy-based models; Numeric optimization routines; Fast and efficient GPU support; Embeddable, with ports to iOS and Android backends	ScalaNLP is the umbrella project for several libraries:; Breeze is a set of libraries for machine learning and numerical computing; Epic is a high-performance statistical parser and structured prediction library
Statistics
GitHub Stars 9.1K	GitHub Stars 3.5K
GitHub Forks 2.4K	GitHub Forks 694
Stacks 355	Stacks 2
Followers 61	Followers 12
Votes 0	Votes 0
Integrations
Python SQLFlow GraphPipe Flair Pythia Databricks Comet.ml	Scala

What are some alternatives to Torch, ScalaNLP?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.