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Difference between revisions of "Algoliterary Encounters"

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==Algoliterary works==
 
==Algoliterary works==
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A selection of works made by the members of Algolit over the past years.
 
* [[Oulipo recipes]]
 
* [[Oulipo recipes]]
 
* [[i-could-have-written-that]]
 
* [[i-could-have-written-that]]
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==Algoliterary explorations==
 
==Algoliterary explorations==
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This chapter presents part of the research of Algolit over the past two years.
 +
 
=== What the Machine Writes: a closer look at the output ===
 
=== What the Machine Writes: a closer look at the output ===
 
* [[CHARNN text generator]]
 
* [[CHARNN text generator]]
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==== From words to numbers ====
 
==== From words to numbers ====
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As machine learning is based on statistics and math, in order to process text, words need to be transformed to numbers. In the following section we present three technologies to do so.
 
* [[A Bag of Words]]
 
* [[A Bag of Words]]
 
* [[A One Hot Vector]]
 
* [[A One Hot Vector]]
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* [[About Word embeddings|Exploring Multidimensional Landscapes: Word Embeddings]]
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* [[Crowd Embeddings|Word Embeddings Casestudy: Crowd embeddings]]
  
==== Exploring Multidimensional Landscapes: Word Embeddings ====
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===== Different vizualisations of word embeddings =====
* [[About Word embeddings]]
 
* [[Crowd Embeddings]]
 
 
 
===== Different portraits of word embeddings =====
 
 
* [[Word embedding Projector]]
 
* [[Word embedding Projector]]
 
* [[The GloVe Reader]]
 
* [[The GloVe Reader]]
  
===== Inspecting the technique =====
+
===== Inspecting the technique behind word embeddings =====
 
* [[word2vec_basic.py]]
 
* [[word2vec_basic.py]]
 
* [[Reverse Algebra]]
 
* [[Reverse Algebra]]
  
 
=== How a Machine Might Speak ===
 
=== How a Machine Might Speak ===
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If a neural net work could speak, what would it say?
 
* [[We Are A Sentiment Thermometer]]
 
* [[We Are A Sentiment Thermometer]]
  

Revision as of 06:49, 2 November 2017

Algoliterary Encounters

Algoliterary works

A selection of works made by the members of Algolit over the past years.

Algoliterary explorations

This chapter presents part of the research of Algolit over the past two years.

What the Machine Writes: a closer look at the output

How the Machine Reads: Dissecting Neural Networks

Datasets

Common public datasets
Algoliterary datasets

From words to numbers

As machine learning is based on statistics and math, in order to process text, words need to be transformed to numbers. In the following section we present three technologies to do so.

Different vizualisations of word embeddings
Inspecting the technique behind word embeddings

How a Machine Might Speak

If a neural net work could speak, what would it say?

Sources