explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-09-21 19:39:13 +03:00
Code Issues Packages Projects Releases Wiki Activity

Make object of the deep learning tutorial clearer

Browse Source 
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`)
This commit is contained in:
Julien Chaumond 2017-04-24 11:55:41 +02:00 committed by GitHub
parent c4be9c36fe
commit f997bceb07
1 changed files with 3 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
website/docs/usage/deep-learning.jade
View File

@ -4,9 +4,9 @@ include ../../_includes/_mixins
p p
| In this example, we'll be using #[+a("https://keras.io/") Keras], as | 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 | it's the most popular deep learning library for Python. Using Keras,
| you've written a custom sentiment analysis model that predicts whether a | we will write a custom sentiment analysis model that predicts whether a
| document is positive or negative. Now you want to find which entities | 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 | are commonly associated with positive or negative documents. Here's a
| quick example of how that can look at runtime. | quick example of how that can look at runtime.