docs(LSA):Changed layout.

Author: antononcube

Tutorials/WolframU-LSAMon-workflows/README.md

@@ -6,104 +6,123 @@ The lectures on
 [Latent Semantic Analysis (LSA)](https://en.wikipedia.org/wiki/Latent_semantic_analysis) 
 are to be recorded through Wolfram University (Wolfram U) in December 2019 and January-February 2020.
 
+-----
 
 ## The lectures (as live-coding sessions)
 
-1. [X] Overview Latent Semantic Analysis (LSA) typical problems and basic workflows.    
-   Answering preliminary anticipated questions.     
-   Here is 
-   [the recording of the first session at YouTube](https://www.youtube.com/watch?v=d5M54_9AMVQ) .
+### 1. [X] Overview Latent Semantic Analysis (LSA) typical problems and basic workflows
+
+Answering preliminary anticipated questions.     
+
+Here is 
+[the recording of the first session at YouTube](https://www.youtube.com/watch?v=d5M54_9AMVQ) .
 
-   - What are the typical applications of LSA?   
-   - Why use LSA?     
-   - What it the fundamental philosophical or scientific assumption for LSA?   
-   - What is the most important and/or fundamental step of LSA?   
-   - What is the difference between LSA and Latent Semantic Indexing (LSI)?   
-   - What are the alternatives?
-     - Using Neural Networks instead?   
-   - How is LSA used to derive similarities between two given texts?   
-   - How is LSA used to evaluate the proximity of phrases?
-     (That have different words, but close semantic meaning.)   
-   - How the main dimension reduction methods compare?
+- What are the typical applications of LSA?   
+- Why use LSA?     
+- What it the fundamental philosophical or scientific assumption for LSA?   
+- What is the most important and/or fundamental step of LSA?   
+- What is the difference between LSA and Latent Semantic Indexing (LSI)?   
+- What are the alternatives?
+  - Using Neural Networks instead?   
+- How is LSA used to derive similarities between two given texts?   
+- How is LSA used to evaluate the proximity of phrases?
+  (That have different words, but close semantic meaning.)   
+- How the main dimension reduction methods compare?
 
-2. [X] LSA for document collections.   
-   Here is [the recording of the second session at YouTube](https://www.youtube.com/watch?v=5pX5WAfPNb8) .
-   
-    - Motivational example -- full blown LSA workflow.
+------
+
+### 2. [X] LSA for document collections
+
+Here is [the recording of the second session at YouTube](https://www.youtube.com/watch?v=5pX5WAfPNb8).
+
+- Motivational example -- full blown LSA workflow.
 
-    - Fundamentals, text transformation (the hard way):
-        - bag of words model,
-        - stop words,
-        - stemming.
+- Fundamentals, text transformation (the hard way):
+  - bag of words model,
+  - stop words,
+  - stemming.
+
+- The easy way with 
+  [LSAMon](https://github.com/antononcube/SimplifiedMachineLearningWorkflows-book/blob/master/Part-2-Monadic-Workflows/A-monad-for-Latent-Semantic-Analysis-workflows.md).
 
-    - The easy way with 
-      [LSAMon](https://github.com/antononcube/SimplifiedMachineLearningWorkflows-book/blob/master/Part-2-Monadic-Workflows/A-monad-for-Latent-Semantic-Analysis-workflows.md).
+- "Eat your own dog food" example.
 
-     - "Eat your own dog food" example.
+------
 
-3. [X] Representation of the documents - the fundamental matrix object.   
-   Here is [the recording of the third session at YouTube](https://www.youtube.com/watch?v=MNQR28P8Juc).
+### 3. [X] Representation of the documents - the fundamental matrix object
+
+Here is [the recording of the third session at YouTube](https://www.youtube.com/watch?v=MNQR28P8Juc).
 
-   - Review: last session's example.
+- Review: last session's example.
 
-   - Review: the motivational example -- full blown LSA workflow.
+- Review: the motivational example -- full blown LSA workflow.
+
+- Linear vector space representation:
+  - LSA's most fundamental operation,
+  - matrix with named rows and columns.
 
-   - Linear vector space representation:
-       - LSA's most fundamental operation,
-       - matrix with named rows and columns.
+- Pareto Principle adherence
+  - for a document,
+  - for a document collection, and
+  - (in general.)
 
-   - Pareto Principle adherence
-       - for a document,
-       - for a document collection, and
-       - (in general.)
+------
 
-4. [X] Representation of unseen documents.   
-   Here is [the recording of the fourth session at YouTube](https://www.youtube.com/watch?v=ElwOLyd9GC4).
+### 4. [X] Representation of unseen documents
+
+Here is [the recording of the fourth session at YouTube](https://www.youtube.com/watch?v=ElwOLyd9GC4).
 
-   - Review: last session's matrix object.
-      - Sparse matrix with named rows and columns.
+- Review: last session's matrix object.
+  - Sparse matrix with named rows and columns.
+
+- Queries representation.
+  
+  - Representing 
+    [rstudio-conf-2019 abstracts](../../Data/RStudio-conf-2019-abstracts.json)
+    in the vector space of 
+    [WTC-2019 abstracts](../../Data/Wolfram-Technology-Conference-2019-abstracts.json).
 
-   - Queries representation.
-     - Representing 
-       [rstudio-conf-2019 abstracts](../../Data/RStudio-conf-2019-abstracts.json)
-       in the vector space of 
-       [WTC-2019 abstracts](../../Data/Wolfram-Technology-Conference-2019-abstracts.json).
+- Making a search engine for
+  
+  - [ ] [Raku's documentation](https://github.com/Raku/doc),    
+  - [X] WTC-2019 abstracts.
 
-   - Making a search engine for
+- Dimension reduction over an image collection.
 
-     - [ ] [Raku's documentation](https://github.com/Raku/doc),    
-     - [X] WTC-2019 abstracts.
+  - Topics over [random mandalas](https://resources.wolframcloud.com/FunctionRepository/resources/RandomMandala).
+  - Representation of unseen mandala images.
 
-   - Dimension reduction over an image collection.
+------
 
-     - Topics over [random mandalas](https://resources.wolframcloud.com/FunctionRepository/resources/RandomMandala).
-     - Representation of unseen mandala images.
+### 5. [X] LSA for image de-noising and classification
 
-5. [X] LSA for image de-noising and classification.   
-   Here is [the recording of the fifth session at YouTube](https://www.youtube.com/watch?v=_KBecGdzoS0).
+Here is [the recording of the fifth session at YouTube](https://www.youtube.com/watch?v=_KBecGdzoS0).
 
-   - Review: last session's image collection topics extraction.
-     - Let us try that two other datasets:
-        - handwritten digits, and
-        - [Hentaigana](https://en.wikipedia.org/wiki/Hentaigana) (maybe).
+- Review: last session's image collection topics extraction.
+  - Let us try that two other datasets:
+    - handwritten digits, and
+    - [Hentaigana](https://en.wikipedia.org/wiki/Hentaigana) (maybe).
 
-   - Image de-noising: 
-     - Using handwritten digits (again).
+- Image de-noising: 
+  - Using handwritten digits (again).
 
-   - Image classification:
-     - Handwritten digits.
+- Image classification:
+  - Handwritten digits.
 
-6. [X] Further use cases.   
-   Here is [the recording of the sixth session at YouTube](https://www.youtube.com/watch?v=Hxawq1O3Oec).
-
-   - Derive a custom taxonomy over a document collection.
-      - Clustering with the reduced dimension.
-   - Apply LSA to Great Conversation studies.	
-   - Use LSA for translation of natural languages.
-     - Using Dostoevsky's novel "The Idiot".
-       - [Russian chapters breakdown](../../Data/Dostoyevsky-The-Idiot-Russian-chapters.json.zip).
-       - [English chapters breakdown](../../Data/Dostoyevsky-The-Idiot-English-chapters.json.zip).
-   - Use LSA for making or improving search engines.
-   - LSA for time series collections.
+------
+
+### 6. [X] Further use cases
+
+Here is [the recording of the sixth session at YouTube](https://www.youtube.com/watch?v=Hxawq1O3Oec).
+
+- Derive a custom taxonomy over a document collection.
+  - Clustering with the reduced dimension.
+- Apply LSA to Great Conversation studies.	
+- Use LSA for translation of natural languages.
+  - Using Dostoevsky's novel "The Idiot".
+    - [Russian chapters breakdown](../../Data/Dostoyevsky-The-Idiot-Russian-chapters.json.zip).
+    - [English chapters breakdown](../../Data/Dostoyevsky-The-Idiot-English-chapters.json.zip).
+- Use LSA for making or improving search engines.
+- LSA for time series collections.