Alternatives to Keras logo

Alternatives to Keras

PyTorch, TensorFlow, MXNet, scikit-learn, and CUDA are the most popular alternatives and competitors to Keras.
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What is Keras and what are its top alternatives?

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
Keras is a tool in the Machine Learning Tools category of a tech stack.
Keras is an open source tool with 49.2K GitHub stars and 18.5K GitHub forks. Here鈥檚 a link to Keras's open source repository on GitHub

Top Alternatives to Keras

Keras alternatives & related posts

PyTorch logo

PyTorch

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A deep learning framework that puts Python first
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Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 972.2K views

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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TensorFlow logo

TensorFlow

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Open Source Software Library for Machine Intelligence
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Conor Myhrvold
Conor Myhrvold
Tech Brand Mgr, Office of CTO at Uber | 8 upvotes 路 972.2K views

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)

See more

In mid-2015, Uber began exploring ways to scale ML across the organization, avoiding ML anti-patterns while standardizing workflows and tools. This effort led to Michelangelo.

Michelangelo consists of a mix of open source systems and components built in-house. The primary open sourced components used are HDFS, Spark, Samza, Cassandra, MLLib, XGBoost, and TensorFlow.

!

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MXNet logo

MXNet

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A flexible and efficient library for deep learning
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PROS OF MXNET
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    CONS OF MXNET
      No cons available

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      scikit-learn logo

      scikit-learn

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      Easy-to-use and general-purpose machine learning in Python
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      PROS OF SCIKIT-LEARN
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      CUDA logo

      CUDA

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      It provides everything you need to develop GPU-accelerated applications
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      PROS OF CUDA
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          ML Kit logo

          ML Kit

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          Machine learning for mobile developers (by Google)
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              TensorFlow.js

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              Machine Learning in JavaScript
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              Kubeflow logo

              Kubeflow

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              Machine Learning Toolkit for Kubernetes
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