Keras vs ml5.js vs TensorFlow

Keras

723
701
+ 1
12
ml5.js

1
29
+ 1
0
TensorFlow

2K
2.1K
+ 1
72
Advice on Keras, ml5.js, and TensorFlow
Adithya Shetty
Student at PES UNIVERSITY · | 5 upvotes · 47.1K views
Needs advice
on
TensorFlow
PyTorch
and
Keras

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

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      What is Keras?

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

      What is ml5.js?

      ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.

      What is 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.
      What companies use Keras?
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      What companies use TensorFlow?
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        What tools integrate with Keras?
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          What are some alternatives to Keras, ml5.js, and TensorFlow?
          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.
          MXNet
          A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
          scikit-learn
          scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
          CUDA
          A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
          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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