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Comet.ml

Stacks5

Followers17

+ 1

Votes1

TensorFlow.js

Stacks63

Followers122

+ 1

Votes6

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Comet.ml vs TensorFlow.js: What are the differences?

Developers describe Comet.ml as "Track, compare and collaborate on Machine Learning experiments". Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility. On the other hand, TensorFlow.js is detailed as "Machine Learning in JavaScript". 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.

Comet.ml and TensorFlow.js can be primarily classified as "Machine Learning" tools.

TensorFlow.js is an open source tool with 11.2K GitHub stars and 816 GitHub forks. Here's a link to TensorFlow.js's open source repository on GitHub.

- No public GitHub repository available -

What is Comet.ml?

Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.

What is 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

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Why do developers choose Comet.ml?

Why do developers choose TensorFlow.js?

1
Best tool for comparing experiments

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3
NodeJS Powered
3
Open Source

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Jobs that mention Comet.ml and TensorFlow.js as a desired skillset

Software Engineer, ML Serving Platform

San Francisco

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Software Engineer, ML Serving Platform

San Francisco

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What are some alternatives to Comet.ml and TensorFlow.js?

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.

Keras

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

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.

ML Kit

ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.

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