fnl en España

A sober perspective on Deep Learning. Published Sat 17 February 2018. This is a short tutorial to compare the outcome of applying Deep Learning techniques to a text classification problem, using word embeddings and a convolutional neural network CNN, via Keras with Theano, for simplicity - but any Keras backend will do, GloVe embeddings, and a SciKit-Learn dataset. The original tutorial is taken from very useful blog. We will work with a CNN to try and beat the best. We will be using Keras. 1 Thean.

OVERVIEW

This domain fnl.es currently has an average traffic classification of zero (the lower the superior). We have probed nine pages within the site fnl.es and found one website linking to fnl.es. There are one mass communication accounts belong to this website.
Pages Crawled
9
Links to this site
1
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1

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FNL.ES HOST

We detected that a single root page on fnl.es took four hundred and two milliseconds to load. We could not detect a SSL certificate, so we consider fnl.es not secure.
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0.402 secs
SSL
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Internet Protocol
192.30.252.153

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PAGE TITLE

fnl en España

DESCRIPTION

A sober perspective on Deep Learning. Published Sat 17 February 2018. This is a short tutorial to compare the outcome of applying Deep Learning techniques to a text classification problem, using word embeddings and a convolutional neural network CNN, via Keras with Theano, for simplicity - but any Keras backend will do, GloVe embeddings, and a SciKit-Learn dataset. The original tutorial is taken from very useful blog. We will work with a CNN to try and beat the best. We will be using Keras. 1 Thean.

CONTENT

This domain fnl.es states the following, "A sober perspective on Deep Learning." We observed that the website also stated " Published Sat 17 February 2018." It also stated " This is a short tutorial to compare the outcome of applying Deep Learning techniques to a text classification problem, using word embeddings and a convolutional neural network CNN, via Keras with Theano, for simplicity - but any Keras backend will do, GloVe embeddings, and a SciKit-Learn dataset. The original tutorial is taken from very useful blog. We will work with a CNN to try and beat the best. We will be using Keras."

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