Microsoft Cognitive Toolkit vs Deepkit

Overview

Microsoft Cognitive Toolkit

Stacks18

Followers21

Votes0

GitHub Stars17.2K

Forks4.4K

Deepkit

Stacks2

Followers8

Votes0

Microsoft Cognitive Toolkit vs Deepkit: What are the differences?

Developers describe Microsoft Cognitive Toolkit as "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. On the other hand, Deepkit is detailed as "The collaborative and analytical AI training suite". It is the collaborative and analytical training suite for insightful, fast, and reproducible modern machine learning. All in one cross-platform desktop app for you alone, corporate or open-source teams.

Microsoft Cognitive Toolkit and Deepkit 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, Deepkit provides the following key features:

Real-time UI and collaboration
Unified experiments
Model debugger

Microsoft Cognitive Toolkit is an open source tool with 16.7K GitHub stars and 4.41K GitHub forks. Here's a link to Microsoft Cognitive Toolkit's open source repository on GitHub.

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Detailed Comparison

Microsoft Cognitive Toolkit	Deepkit
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.	It is the collaborative and analytical training suite for insightful, fast, and reproducible modern machine learning. All in one cross-platform desktop app for you alone, corporate or open-source teams.
Speed & Scalability; Commercial-Grade Quality; Easy-to-use architecture	Real-time UI and collaboration; Unified experiments; Model debugger; Any framework, all languages; Job scheduling; Pipeling; Docker and GPU support; Docker and GPU support; Offline first; Git integration / CI
Statistics
GitHub Stars 17.2K	GitHub Stars -
GitHub Forks 4.4K	GitHub Forks -
Stacks 18	Stacks 2
Followers 21	Followers 8
Votes 0	Votes 0
Integrations
C++ Python	Docker Python TensorFlow Git Keras PyTorch

What are some alternatives to Microsoft Cognitive Toolkit, Deepkit?

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.