Algoliterary Encounters: Difference between revisions
From Algolit
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* [[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'. | ||
| − | === | + | ===Dissecting Neural Networks=== |
==== Datasets ==== | ==== Datasets ==== | ||
Revision as of 14:20, 25 October 2017
Start of the Algoliterary Encounters catalog.
Introduction
Algoliterary works
- Oulipo scripts
- i-could-have-written-that
- Obama, model for a politician
- In the company of CluebotNG
Algoliterary explorations
A few outputs to see how it works
- 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'.
Dissecting Neural Networks
Datasets
- Many many words - introduction to the datasets with calculation exercise
- The data (e)speaks - espeak installation
- The Enron email archive
- Common Crawl
- Frankenstein
- Learning from Deep Learning
- HG Wells personal dataset
- Jules Verne (FR), Shakespeare (FR) -> download from Gutenberg & clean up
- AnarchFem
- WikiHarass
- Tristes Tropiques
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
Autonomous machine as inspection
Algoliterary Toolkit
Bibliography
- Algoliterary Bibliography - Reading Room texts