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Keras
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Keras vs Paperspace: What are the differences?

Keras: Deep Learning library for Theano and TensorFlow. Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/; Paperspace: The way to access and manage limitless computing power in the cloud. It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.

Keras and Paperspace belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Keras are:

  • neural networks API
  • Allows for easy and fast prototyping
  • Convolutional networks support

On the other hand, Paperspace provides the following key features:

  • Intelligent alert
  • Two-factor authentication
  • Share drives

Keras is an open source tool with 44.1K GitHub stars and 16.8K GitHub forks. Here's a link to Keras's open source repository on GitHub.

- No public GitHub repository available -

What is Keras?

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

What is Paperspace?

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.
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        What are some alternatives to Keras and Paperspace?
        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.
        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.
        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.
        See all alternatives
        Decisions about Keras and Paperspace
        Conor Myhrvold
        Conor Myhrvold
        Tech Brand Mgr, Office of CTO at Uber | 6 upvotes 513.4K views
        atUber TechnologiesUber Technologies
        TensorFlow
        TensorFlow
        Keras
        Keras
        PyTorch
        PyTorch

        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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