Algoliterary Encounters: Difference between revisions
From Algolit
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== Sources == | == Sources == | ||
| − | + | *[https://gitlab.constantvzw.org/algolit/algolit/tree/master/algoliterary_encounter Algoliterary Gitlab] | |
| − | * [ | + | *[https://gitlab.constantvzw.org/algolit/algolit/tree/master/algoliterary_encounter/algoliterary-toolkit/cgi-example jinja-cgi interface template] |
| − | * [ | + | *[https://gitlab.constantvzw.org/algolit/algolit/blob/master/algoliterary_encounter/algoliterary-toolkit/text-punctuation-clean-up.py text-punctuation-clean-up.py] |
===Bibliography=== | ===Bibliography=== | ||
Revision as of 14:36, 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