blog.fastforwardlabs.com/2016/02/24/hello-world-in-keras-or-scikit-learn-versus-keras.html

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https://blog.fastforwardlabs.com/2016/02/24/hello-world-in-keras-or-scikit-learn-versus-keras.html

"Hello world" in Keras (or, Scikit-learn versus Keras)

This article is available as a notebook on Github. Please refer to that notebook for a more detailed discussion and code fixes and updates. Despite all the recent excitement around deep learning, neural networks have a reputation among non-specialists as complicated to build and difficult to interpret. And while interpretability remains an issue, there are now high-level neural network libraries that enable developers to quickly build neural network models without worrying about the numerical details of floating point operations and linear algebra.



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"Hello world" in Keras (or, Scikit-learn versus Keras)

https://blog.fastforwardlabs.com/2016/02/24/hello-world-in-keras-or-scikit-learn-versus-keras.html

This article is available as a notebook on Github. Please refer to that notebook for a more detailed discussion and code fixes and updates. Despite all the recent excitement around deep learning, neural networks have a reputation among non-specialists as complicated to build and difficult to interpret. And while interpretability remains an issue, there are now high-level neural network libraries that enable developers to quickly build neural network models without worrying about the numerical details of floating point operations and linear algebra.



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https://blog.fastforwardlabs.com/2016/02/24/hello-world-in-keras-or-scikit-learn-versus-keras.html

"Hello world" in Keras (or, Scikit-learn versus Keras)

This article is available as a notebook on Github. Please refer to that notebook for a more detailed discussion and code fixes and updates. Despite all the recent excitement around deep learning, neural networks have a reputation among non-specialists as complicated to build and difficult to interpret. And while interpretability remains an issue, there are now high-level neural network libraries that enable developers to quickly build neural network models without worrying about the numerical details of floating point operations and linear algebra.

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      This article is available as a notebook on Github. Please refer to that notebook for a more detailed discussion and code fixes and updates. Despite all the recent excitement around deep learning, neural networks have a reputation among non-specialists as complicated to build and difficult to interpret. And while interpretability remains an issue, there are now high-level neural network libraries that enable developers to quickly build neural network models without worrying about the numerical details of floating point operations and linear algebra.
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