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  1. Introducing Keras 2


    Keras was released two years ago, in March 2015. It then proceeded to grow from one user to one hundred thousand.

    Keras user growth

    Hundreds of people have contributed to the Keras codebase. Many thousands have contributed to the community. Keras has enabled new startups, made researchers more productive, simplified the workflows of ...

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  2. Building Autoencoders in Keras

    In this tutorial, we will answer some common questions about autoencoders, and we will cover code examples of the following models:

    • a simple autoencoder based on a fully-connected layer
    • a sparse autoencoder
    • a deep fully-connected autoencoder
    • a deep convolutional autoencoder
    • an image denoising model
    • a sequence-to-sequence autoencoder
    • a variational autoencoder ...
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  3. Introducing Keras 1.0

    Keras was initially released a year ago, late March 2015. It has made tremendous progress since, both on the development front, and as a community.

    But continuous improvement isn't enough. A year of developing Keras, using Keras, and getting feedback from thousands of users has taught us a lot ...

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  4. Keras, now running on TensorFlow

    The purpose of Keras is to be a model-level framework, providing a set of "Lego blocks" for building Deep Learning models in a fast and straightforward way. Among Deep Learning frameworks, Keras is resolutely high up on the ladder of abstraction.

    As such, Keras does not handle itself low-level tensor ...

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