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  1. Stackups
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  4. Machine Learning Tools
  5. Caffe vs Microsoft Cognitive Toolkit

Caffe vs Microsoft Cognitive Toolkit

OverviewComparisonAlternatives

Overview

Caffe
Caffe
Stacks66
Followers73
Votes0
GitHub Stars34.7K
Forks18.6K
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit
Stacks18
Followers21
Votes0
GitHub Stars17.2K
Forks4.4K

Microsoft Cognitive Toolkit vs Caffe: 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 Caffe? A deep learning framework. It is a deep learning framework made with expression, speed, and modularity in mind.

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

Some of the features offered by Microsoft Cognitive Toolkit are:

  • Speed & Scalability
  • Commercial-Grade Quality
  • Easy-to-use architecture

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

  • Extensible code
  • Speed
  • Community

Microsoft Cognitive Toolkit and Caffe are both open source tools. It seems that Caffe with 29.2K GitHub stars and 17.6K forks on GitHub has more adoption than Microsoft Cognitive Toolkit with 16.5K GitHub stars and 4.39K GitHub forks.

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Detailed Comparison

Caffe
Caffe
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit

It is a deep learning framework made with expression, speed, and modularity in mind.

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.

Extensible code; Speed; Community;
Speed & Scalability; Commercial-Grade Quality; Easy-to-use architecture
Statistics
GitHub Stars
34.7K
GitHub Stars
17.2K
GitHub Forks
18.6K
GitHub Forks
4.4K
Stacks
66
Stacks
18
Followers
73
Followers
21
Votes
0
Votes
0
Integrations
TensorFlow
TensorFlow
Keras
Keras
Amazon SageMaker
Amazon SageMaker
Pythia
Pythia
C++
C++
Python
Python

What are some alternatives to Caffe, 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.

scikit-learn

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

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.

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