Keras聽vs聽scikit-learn聽vs聽TensorFlow

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Keras
Keras

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scikit-learn
scikit-learn

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TensorFlow
TensorFlow

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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.

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.
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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.
ML Kit
ML Kit brings Google鈥檚 machine learning expertise to mobile developers in a powerful and easy-to-use package.
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.
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
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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Decisions about Keras, scikit-learn, and TensorFlow
Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 6 upvotes 436.7K views
atUber TechnologiesUber Technologies
PyTorch
PyTorch
Keras
Keras
TensorFlow
TensorFlow

Why we built an open source, distributed training framework for TensorFlow , Keras , and PyTorch:

At Uber, we apply deep learning across our business; from self-driving research to trip forecasting and fraud prevention, deep learning enables our engineers and data scientists to create better experiences for our users.

TensorFlow has become a preferred deep learning library at Uber for a variety of reasons. To start, the framework is one of the most widely used open source frameworks for deep learning, which makes it easy to onboard new users. It also combines high performance with an ability to tinker with low-level model details鈥攆or instance, we can use both high-level APIs, such as Keras, and implement our own custom operators using NVIDIA鈥檚 CUDA toolkit.

Uber has introduced Michelangelo (https://eng.uber.com/michelangelo/), an internal ML-as-a-service platform that democratizes machine learning and makes it easy to build and deploy these systems at scale. In this article, we pull back the curtain on Horovod, an open source component of Michelangelo鈥檚 deep learning toolkit which makes it easier to start鈥攁nd speed up鈥攄istributed deep learning projects with TensorFlow:

https://eng.uber.com/horovod/

(Direct GitHub repo: https://github.com/uber/horovod)

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Eliana Abraham uses TensorFlowTensorFlow

Machine Learning in EECS 445

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Eliana Abraham uses scikit-learnscikit-learn

Machine Learning in EECS 445

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Taylor Host uses TensorFlowTensorFlow

Pilot integration for retraining.

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