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  1. Stackups
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  5. Microsoft Cognitive Toolkit vs Tensorflow Lite

Microsoft Cognitive Toolkit vs Tensorflow Lite

OverviewComparisonAlternatives

Overview

Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit
Stacks18
Followers21
Votes0
GitHub Stars17.2K
Forks4.4K
Tensorflow Lite
Tensorflow Lite
Stacks74
Followers144
Votes1

Microsoft Cognitive Toolkit vs Tensorflow Lite: 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 Tensorflow Lite? Deploy machine learning models on mobile and IoT devices. It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.

Microsoft Cognitive Toolkit and Tensorflow Lite can be categorized 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, Tensorflow Lite provides the following key features:

  • Lightweight solution for mobile and embedded devices
  • Enables low-latency inference of on-device machine learning models with a small binary size
  • Fast performance

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
Microsoft Cognitive Toolkit
Tensorflow Lite
Tensorflow Lite

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 a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.

Speed & Scalability; Commercial-Grade Quality; Easy-to-use architecture
Lightweight solution for mobile and embedded devices; Enables low-latency inference of on-device machine learning models with a small binary size; Fast performance
Statistics
GitHub Stars
17.2K
GitHub Stars
-
GitHub Forks
4.4K
GitHub Forks
-
Stacks
18
Stacks
74
Followers
21
Followers
144
Votes
0
Votes
1
Pros & Cons
No community feedback yet
Pros
  • 1
    .tflite conversion
Integrations
C++
C++
Python
Python
Python
Python
Android OS
Android OS
iOS
iOS
Raspberry Pi
Raspberry Pi

What are some alternatives to Microsoft Cognitive Toolkit, Tensorflow Lite?

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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