Keras vs scikit-learn vs TensorFlow

Keras

682
655
+ 1
12
scikit-learn

651
642
+ 1
28
TensorFlow

1.9K
2K
+ 1
72

Keras vs TensorFlow vs scikit-learn: What are the differences?

Tensorflow is the most famous library in production for deep learning models. Offers automatic differentiation to perform backpropagation smoothly, allowing you to literally build any machine learning model literally. Keras is a high-level API built on Tensorflow. It is user-friendly and helps quickly build and test a neural network with minimal lines of code. Like building simple or complex neural networks within a few minutes. Modular since everything in Keras can be represented as modules. Scikit Learn is a general machine learning library built on top of NumPy. It features a lot of utilities for general pre and post-processing of data. It is a library in Python used to construct traditional models.

Advice on Keras, scikit-learn, and TensorFlow
Adithya Shetty
Adithya Shetty
Student at PES UNIVERSITY · | 5 upvotes · 19K views
Needs advice
on
TensorFlowTensorFlowPyTorchPyTorch
and
KerasKeras

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 scikit-learn?

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

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?
What companies use scikit-learn?
What companies use TensorFlow?

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What tools integrate with Keras?
What tools integrate with scikit-learn?
What tools integrate with TensorFlow?

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What are some alternatives to Keras, scikit-learn, 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.
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.
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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