README

README.md

@@ -51,7 +51,7 @@ As shown in the image below, this will open up, locally, an interactive tool in
 
 ![img](https://raw.githubusercontent.com/sergioburdisso/pyss3/master/docs/_static/ss3_live_test.gif)
 
-For example, we have uploaded two of these live tests online for you to try out: ["Movie Review Classification"](http://tworld.io/ss3/live_test_online/#30305) and ["Topic Categorization"](http://tworld.io/ss3/live_test_online/#30303), both were obtained following the [tutorials](https://pyss3.readthedocs.io#tutorials).
+For example, we have uploaded two of these live tests online for you to try out: ["Movie Review Classification"](http://tworld.io/ss3/live_test_online/#30305) and ["Topic Categorization"](http://tworld.io/ss3/live_test_online/#30303), both were obtained following the [tutorials](https://pyss3.readthedocs.io/en/latest/home/short-description.html#tutorials).
 
 ### And last but not least, the ``PySS3 Command Line``
 
@@ -68,7 +68,7 @@ In this illustrative example, `s` will take 6 different values between .2 and .8
 ```
 ![img](https://raw.githubusercontent.com/sergioburdisso/pyss3/master/docs/_static/plot_evaluations.gif)
 
-Each dot represents an experiment/evaluation performed using that particular combination of values (s, l, and p). Also, dots are painted proportional to how good the performance was using that configuration of the model. Researchers can interactively change the evaluation metrics to be used (accuracy, precision, recall, f1, etc.) and plots will update "on the fly". Additionally, when the cursor is moved over a data point, useful information is shown (including a "compact" representation of the confusion matrix obtained in that experiment). Finally, it is worth mentioning that, before showing the 3D plots, PySS3 creates and save a single and portable HTML file in your project folder containing the interactive plots. This allows researchers to store, send or upload the plots to another place using this single HTML file (or even provide a link to this file in their own papers, which would be nicer for readers, plus it would increase experimentation transparency). For example, we have uploaded two of these files for you to see: ["Movie Review Classification"](http://tworld.io/ss3/ss3_model_evaluation[movie_review_3grams].html) and ["Topic Categorization"](http://tworld.io/ss3/ss3_model_evaluation[topics_3grams].html), both evaluation plots were also obtained following the [tutorials](https://pyss3.readthedocs.io#tutorials).
+Each dot represents an experiment/evaluation performed using that particular combination of values (s, l, and p). Also, dots are painted proportional to how good the performance was using that configuration of the model. Researchers can interactively change the evaluation metrics to be used (accuracy, precision, recall, f1, etc.) and plots will update "on the fly". Additionally, when the cursor is moved over a data point, useful information is shown (including a "compact" representation of the confusion matrix obtained in that experiment). Finally, it is worth mentioning that, before showing the 3D plots, PySS3 creates and save a single and portable HTML file in your project folder containing the interactive plots. This allows researchers to store, send or upload the plots to another place using this single HTML file (or even provide a link to this file in their own papers, which would be nicer for readers, plus it would increase experimentation transparency). For example, we have uploaded two of these files for you to see: ["Movie Review Classification"](http://tworld.io/ss3/ss3_model_evaluation[movie_review_3grams].html) and ["Topic Categorization"](http://tworld.io/ss3/ss3_model_evaluation[topics_3grams].html), both evaluation plots were also obtained following the [tutorials](https://pyss3.readthedocs.io/en/latest/home/short-description.html#tutorials).
 
 
 ## The PySS3 Workflow

docs/index.rst

@@ -56,9 +56,7 @@ allow you to visualize and understand what your model is actually
 learning.
 
 .. figure:: _static/ss3_live_test.gif
-   :alt: img
 
-   img
 For example, we have uploaded two of these live tests online for you to
 try out: `"Movie Review
 Classification" <http://tworld.io/ss3/live_test_online/#30305>`__ and
@@ -103,9 +101,7 @@ interactive 3D plot in the browser:
     (pyss3) >>> plot evaluations
 
 .. figure:: _static/plot_evaluations.gif
-   :alt: img
 
-   img
 Each dot represents an experiment/evaluation performed using that
 particular combination of values (s, l, and p). Also, dots are painted
 proportional to how good the performance was using that configuration of