ENorm vs Microsoft Cognitive Toolkit

Overview

Microsoft Cognitive Toolkit

Stacks18

Followers21

Votes0

GitHub Stars17.2K

Forks4.4K

ENorm

Stacks1

Followers9

Votes0

GitHub Stars115

Forks13

ENorm vs Microsoft Cognitive Toolkit: What are the differences?

ENorm: Equi-normalization of Neural Networks (by Facebook). A fast and iterative method for minimizing the L2 norm of the weights of a given neural network that provably converges to a unique solution; Microsoft Cognitive Toolkit: An open-source toolkit for deep learning. It is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph.

ENorm and Microsoft Cognitive Toolkit can be primarily classified as "Machine Learning" tools.

ENorm and Microsoft Cognitive Toolkit are both open source tools. Microsoft Cognitive Toolkit with 16.3K GitHub stars and 4.34K forks on GitHub appears to be more popular than ENorm with 107 GitHub stars and 9 GitHub forks.

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Detailed Comparison

Microsoft Cognitive Toolkit	ENorm
It is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph.	A fast and iterative method for minimizing the L2 norm of the weights of a given neural network that provably converges to a unique solution.
Speed & Scalability; Commercial-Grade Quality; Easy-to-use architecture	Asymmetric scaling; Python 3.6 and latest support;
Statistics
GitHub Stars 17.2K	GitHub Stars 115
GitHub Forks 4.4K	GitHub Forks 13
Stacks 18	Stacks 1
Followers 21	Followers 9
Votes 0	Votes 0
Integrations
C++ Python	No integrations available

What are some alternatives to Microsoft Cognitive Toolkit, ENorm?

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.