Algoliterary Encounters: Difference between revisions
From Algolit
| Line 16: | Line 16: | ||
==Algoliterary explorations== | ==Algoliterary explorations== | ||
=== 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== |
* [[You shall know a word by the company it keeps]] - Five word2vec graphs, each of them containing the words 'collective', 'being' and 'social'. | * [[You shall know a word by the company it keeps]] - Five word2vec graphs, each of them containing the words 'collective', 'being' and 'social'. | ||
Revision as of 15:15, 25 October 2017
Start of the Algoliterary Encounters catalog.
Introduction
Algoliterary works
- Oulipo recipes
- i-could-have-written-that
- Obama, model for a politician
- In the company of CluebotNG
Algoliterary explorations
What the Machine Writes: a closer look at the output
- [[CHARNN text generator==
- You shall know a word by the company it keeps - Five word2vec graphs, each of them containing the words 'collective', 'being' and 'social'.
How the Machine Reads: Dissecting Neural Networks
Datasets
- Many many words - introduction to the datasets with calculation exercise
- The data (e)speaks - espeak installation
From words to numbers
Different views on the data
Creating word embeddings using word2vec
- Crowd Embeddings - case studies, still needs fine tuning
- word2vec_basic.py - in piles of paper
- softmax annotated
- Reverse Algebra
How a Machine Might Speak
Sources
Bibliography
- Algoliterary Bibliography - Reading Room texts