From f997bceb07a3755302f90d2779a87a7c954309c5 Mon Sep 17 00:00:00 2001 From: Julien Chaumond Date: Mon, 24 Apr 2017 11:55:41 +0200 Subject: [PATCH] Make object of the deep learning tutorial clearer This is a great tutorial, but I think it is weirdly explained in the current form. The largest part of the code is about implementing the actual sentiment analysis model, not about counting entities. (which is not even present in the `deep_learning_keras.py` script in `examples`) --- website/docs/usage/deep-learning.jade | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/website/docs/usage/deep-learning.jade b/website/docs/usage/deep-learning.jade index a1252a4fe..fec01b4ba 100644 --- a/website/docs/usage/deep-learning.jade +++ b/website/docs/usage/deep-learning.jade @@ -4,9 +4,9 @@ include ../../_includes/_mixins p | In this example, we'll be using #[+a("https://keras.io/") Keras], as - | it's the most popular deep learning library for Python. Let's assume - | you've written a custom sentiment analysis model that predicts whether a - | document is positive or negative. Now you want to find which entities + | it's the most popular deep learning library for Python. Using Keras, + | we will write a custom sentiment analysis model that predicts whether a + | document is positive or negative. Then, we will use it to find which entities | are commonly associated with positive or negative documents. Here's a | quick example of how that can look at runtime.