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Zuzanna_Klyszejko_Vango
- 1. Van Go Your personal art curator Zuzanna Klyszejko
- 2. Problem When you’re visiting a new place your time is limited
- 3. Problem When you’re visiting a new place your time is limited There is too much to see
- 4. Problem When you’re visiting a new place your time is limited There is too much to see You need to make a choice
- 5. If a visitor wants to see only landscapes and plants, there should be a way to choose a subset of museum objects that makes the most of their time
- 6. ”This painting was said by him to have been inspired by the work of Li Cheng, a tenth-century landscape artist. Its spare, rather dry brushwork again repeats the deliberately simple, austere quality that is the feature of many so-called literati paintings. Hanging scroll. Landscape. Bare trees in winter, with reference to Li Cheng (919-67) and a river. Painted in a very dry style. Inscriptions and seals. Ink on paper.” 100 000 curatorial descriptions of paintings and drawings from British Museum’s database
- 7. ”this painting be say by him to have be inspire by the work of li cheng , a tenth - century landscape artist . its spare , rather dry brushwork again repeat the deliberately simple , austere quality that be the feature of many so - call literati painting . hang scroll . landscape . bare tree in winter , with reference to li cheng ( 919 - 67 ) and a river . paint in a very dry style . inscription and seal . ink on paper.” tokenizing and lemmatizing 100 000 curatorial descriptions of paintings and drawings from British Museum’s database
- 8. tokenizing and lemmatizing Count Vectorizer + TFIDF 100 000 curatorial descriptions of paintings and drawings from British Museum’s database
- 9. tokenizing and lemmatizing Count Vectorizer + TFIDF Latent Semantic Analysis (PCA) 100 000 curatorial descriptions of paintings and drawings from British Museum’s database
- 10. tokenizing and lemmatizing Count Vectorizer + TFIDF Latent Semantic Analysis (PCA) Cosine similarity 100 000 curatorial descriptions of paintings and drawings from British Museum’s database
- 11. How does it work in practice? Demo
- 19. “landscape” K-fold cross validation to verify the model using Jaccard index
- 20. Dataset 1 Train a model Dataset 2 Train a model Dataset 3 Train a model K-fold cross validation to verify the model using Jaccard index “landscape”
- 21. About me Undergraduate and Master’s in Cognitive Neuroscience in Poland Phd in Cognitive Neuroscience (NYU) Hiker & art lover
Editor's Notes
- At a museum there is an overabundance of art. Unless you have unlimited time, you need to make choices. Usually museums provide a list of highlights, must-see objects which are famous. But what if you want to explore a selection of pieces related to something which fascinates you, across times and cultures? This posits an interesting challenge which I was excited to tackle. My thinking was the following: nowadays, we are used to searching using words, like with popular search engines. Why not use a similar way to plan your visit at a museum? Traditional searches are not useful here since they are based on models which are very broad. I wanted to use expert knowledge. Using unsupervised machine learning algorithms I identified clusters of paintings and based on those relationships I
- It assumes that similar words will occur in similar pieces of text. Text data is in a high dimensional space which is sparsely populated. In such situation, words such as “feline” and “cat” are orthogonal - this is why it’s good to reduce the number of dimensions using PCA.
- It assumes that similar words will occur in similar pieces of text. Text data is in a high dimensional space which is sparsely populated. In such situation, words such as “feline” and “cat” are orthogonal - this is why it’s good to reduce the number of dimensions using PCA.
- It assumes that similar words will occur in similar pieces of text. Text data is in a high dimensional space which is sparsely populated. In such situation, words such as “feline” and “cat” are orthogonal - this is why it’s good to reduce the number of dimensions using PCA.
- It assumes that similar words will occur in similar pieces of text. Text data is in a high dimensional space which is sparsely populated. In such situation, words such as “feline” and “cat” are orthogonal - this is why it’s good to reduce the number of dimensions using PCA.
- It assumes that similar words will occur in similar pieces of text. Text data is in a high dimensional space which is sparsely populated. In such situation, words such as “feline” and “cat” are orthogonal - this is why it’s good to reduce the number of dimensions using PCA.
- Intuitively you can tell that cosine similarity in the PCA space represents semantic similarity in the specific domain. Here, I plotted lists of words which my model returns when queried with words on the top of the list. Just by visual inspection we can tell that these results are reasonable. One question we have to ask ourselves, though, is whether these similarities are a result of overfitting or more robust and consistent qualities of the data.
- Intuitively you can tell that cosine similarity in the PCA space represents semantic similarity in the specific domain. Here, I plotted lists of words which my model returns when queried with words on the top of the list. Just by visual inspection we can tell that these results are reasonable. One question we have to ask ourselves, though, is whether these similarities are a result of overfitting or more robust and consistent qualities of the data.
- Intuitively you can tell that cosine similarity in the PCA space represents semantic similarity in the specific domain. Here, I plotted lists of words which my model returns when queried with words on the top of the list. Just by visual inspection we can tell that these results are reasonable. One question we have to ask ourselves, though, is whether these similarities are a result of overfitting or more robust and consistent qualities of the data.
- Intuitively you can tell that cosine similarity in the PCA space represents semantic similarity in the specific domain. Here, I plotted lists of words which my model returns when queried with words on the top of the list. Just by visual inspection we can tell that these results are reasonable. One question we have to ask ourselves, though, is whether these similarities are a result of overfitting or more robust and consistent qualities of the data.
- Intuitively you can tell that cosine similarity in the PCA space represents semantic similarity in the specific domain. Here, I plotted lists of words which my model returns when queried with words on the top of the list. Just by visual inspection we can tell that these results are reasonable. One question we have to ask ourselves, though, is whether these similarities are a result of overfitting or more robust and consistent qualities of the data.
- To verify that, I validated my model by running it on several subsets of my dataset and compared lists of similar words.
- To verify that, I validated my model by running it on several subsets of my dataset and compared lists of similar words.
- It assumes that similar words will occur in similar pieces of text. Text data is in a high dimensional space which is sparsely populated. In such situation, words such as “feline” and “cat” are orthogonal - this is why it’s good to reduce the number of dimensions using PCA.
- It assumes that similar words will occur in similar pieces of text. Text data is in a high dimensional space which is sparsely populated. In such situation, words such as “feline” and “cat” are orthogonal - this is why it’s good to reduce the number of dimensions using PCA.