Algoliterary Encounters: Difference between revisions
From Algolit
m |
m |
||
| Line 5: | Line 5: | ||
==Algoliterary works== | ==Algoliterary works== | ||
| + | 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]] | ||
| Line 11: | Line 12: | ||
==Algoliterary explorations== | ==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 === | === What the Machine Writes: a closer look at the output === | ||
* [[CHARNN text generator]] | * [[CHARNN text generator]] | ||
| Line 31: | Line 34: | ||
==== From words to numbers ==== | ==== 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. | ||
* [[A Bag of Words]] | * [[A Bag of Words]] | ||
* [[A One Hot Vector]] | * [[A One Hot Vector]] | ||
| + | * [[About Word embeddings|Exploring Multidimensional Landscapes: Word Embeddings]] | ||
| + | * [[Crowd Embeddings|Word Embeddings Casestudy: Crowd embeddings]] | ||
| − | + | ===== Different vizualisations of word embeddings ===== | |
| − | |||
| − | |||
| − | |||
| − | ===== Different | ||
* [[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 === | ||
| + | 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.
- Oulipo recipes
- i-could-have-written-that
- The Weekly Address, A model for a politician
- In the company of CluebotNG
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.
- A Bag of Words
- A One Hot Vector
- Exploring Multidimensional Landscapes: Word Embeddings
- Word Embeddings Casestudy: Crowd embeddings
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?