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  5. Microsoft Cognitive Toolkit vs scikit-learn

Microsoft Cognitive Toolkit vs scikit-learn

OverviewComparisonAlternatives

Overview

scikit-learn
scikit-learn
Stacks1.3K
Followers1.1K
Votes45
GitHub Stars63.9K
Forks26.4K
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit
Stacks18
Followers21
Votes0
GitHub Stars17.2K
Forks4.4K

Microsoft Cognitive Toolkit vs scikit-learn: What are the differences?

What is 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.

What is scikit-learn? Easy-to-use and general-purpose machine learning in Python. scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

Microsoft Cognitive Toolkit and scikit-learn can be categorized as "Machine Learning" tools.

Microsoft Cognitive Toolkit and scikit-learn are both open source tools. scikit-learn with 36.5K GitHub stars and 17.9K forks on GitHub appears to be more popular than Microsoft Cognitive Toolkit with 16.3K GitHub stars and 4.34K GitHub forks.

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Detailed Comparison

scikit-learn
scikit-learn
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit

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

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.

-
Speed & Scalability; Commercial-Grade Quality; Easy-to-use architecture
Statistics
GitHub Stars
63.9K
GitHub Stars
17.2K
GitHub Forks
26.4K
GitHub Forks
4.4K
Stacks
1.3K
Stacks
18
Followers
1.1K
Followers
21
Votes
45
Votes
0
Pros & Cons
Pros
  • 26
    Scientific computing
  • 19
    Easy
Cons
  • 2
    Limited
No community feedback yet
Integrations
No integrations available
C++
C++
Python
Python

What are some alternatives to scikit-learn, Microsoft Cognitive Toolkit?

TensorFlow

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.

PyTorch

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

Keras

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

Kubeflow

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

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

Polyaxon

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

Streamlit

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

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

H2O

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.

PredictionIO

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.

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