diff --git a/docs/examples/01-exploring-wellcome-collections-apis.md b/docs/examples/01-exploring-wellcome-collections-apis.md index 83bcc02..367b52d 100644 --- a/docs/examples/01-exploring-wellcome-collections-apis.md +++ b/docs/examples/01-exploring-wellcome-collections-apis.md @@ -1,6 +1,6 @@ # 1. Exploring Wellcome Collection's APIs -[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/01-exploring-wellcome-collections-apis.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/01-exploring-wellcome-collections-apis.ipynb) +[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/01-exploring-wellcome-collections-apis.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/01-exploring-wellcome-collections-apis.ipynb) Wellcome collection has a few public APIs which can be used to fetch things like works, images, and concepts. They all live behind the following base URL diff --git a/docs/examples/02-extracting-more-data-for-local-analysis.md b/docs/examples/02-extracting-more-data-for-local-analysis.md index 9bea3ff..ba290c9 100644 --- a/docs/examples/02-extracting-more-data-for-local-analysis.md +++ b/docs/examples/02-extracting-more-data-for-local-analysis.md @@ -1,6 +1,6 @@ # 2. Extracting more data for local analysis -[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/02-extracting-more-data-for-local-analysis.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/02-extracting-more-data-for-local-analysis.ipynb) +[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/02-extracting-more-data-for-local-analysis.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/02-extracting-more-data-for-local-analysis.ipynb) In the last notebook, we saw that the `/works` API can do some clever querying and filtering. However, we often have questions which can't be answered by the API by itself. In those cases, it's useful to collect a load of data from the API and then analyse it locally. diff --git a/docs/examples/03-connecting-the-apis-together.md b/docs/examples/03-connecting-the-apis-together.md index 63586d1..d8e6a86 100644 --- a/docs/examples/03-connecting-the-apis-together.md +++ b/docs/examples/03-connecting-the-apis-together.md @@ -1,6 +1,6 @@ # 3. Connecting the APIs together -[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/03-connecting-the-apis-together.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/03-connecting-the-apis-together.ipynb) +[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/03-connecting-the-apis-together.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/03-connecting-the-apis-together.ipynb) So far, we've only looked at the `/works` API, but Wellcome Collection has a few more which we can make use of. As well as `/works`, we can also use `/images` and `/concepts`. diff --git a/docs/examples/04-building-graphs-of-visually-similar-images.md b/docs/examples/04-building-graphs-of-visually-similar-images.md index 05e4ae6..3dd49b1 100644 --- a/docs/examples/04-building-graphs-of-visually-similar-images.md +++ b/docs/examples/04-building-graphs-of-visually-similar-images.md @@ -1,6 +1,6 @@ # 4. Building graphs of visually similar images -[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/04-building-graphs-of-visually-similar-images.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/04-building-graphs-of-visually-similar-images.ipynb) +[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/04-building-graphs-of-visually-similar-images.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/04-building-graphs-of-visually-similar-images.ipynb) In the last notebook, we introduced the ability to fetch visually similar images using the `/images` API. diff --git a/docs/examples/05-working-with-snapshots-of-the-api.md b/docs/examples/05-working-with-snapshots-of-the-api.md index e94731e..f821236 100644 --- a/docs/examples/05-working-with-snapshots-of-the-api.md +++ b/docs/examples/05-working-with-snapshots-of-the-api.md @@ -1,6 +1,6 @@ # 5. Working with snapshots of the API -[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/05-working-with-snapshots-of-the-api.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/05-working-with-snapshots-of-the-api.ipynb) +[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/05-working-with-snapshots-of-the-api.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/05-working-with-snapshots-of-the-api.ipynb) As we saw at the end of the last notebook, the API limits its responses to 10,000 total results - after that point, users are directed to work with the snapshots. For example, making a request to [https://api.wellcomecollection.org/catalogue/v2/works?pageSize=100&page=101](https://api.wellcomecollection.org/catalogue/v2/works?pageSize=100&page=101) gives us: diff --git a/docs/examples/06-visualising-the-collection-on-a-map.md b/docs/examples/06-visualising-the-collection-on-a-map.md index 617a944..42b5430 100644 --- a/docs/examples/06-visualising-the-collection-on-a-map.md +++ b/docs/examples/06-visualising-the-collection-on-a-map.md @@ -1,6 +1,6 @@ # 6. Visualising the collections on a map -[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/06-visualising-the-collection-on-a-map.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/06-visualising-the-collection-on-a-map.ipynb) +[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/06-visualising-the-collection-on-a-map.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/06-visualising-the-collection-on-a-map.ipynb) In this notebook, we're going to use a secondary API to visualise the geographical extent of the collection on a map. diff --git a/docs/examples/07-building-an-image-classifier.md b/docs/examples/07-building-an-image-classifier.md index 488e17a..b6bee3f 100644 --- a/docs/examples/07-building-an-image-classifier.md +++ b/docs/examples/07-building-an-image-classifier.md @@ -1,6 +1,6 @@ # 7. Building an image classifier -[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/07-building-an-image-classifier.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/07-building-an-image-classifier.ipynb) +[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/07-building-an-image-classifier.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/07-building-an-image-classifier.ipynb) This notebook is going to race through some high-level concepts in machine learning (specifically, fine-tuning a convolutional neural network). However, our focus will remain on demonstrating the practical uses of the Wellcome Collection API. As such, some important ML topics will be covered in less detail than they deserve, and some will be skipped entirely. If you're not already familiar with the basics of ML but want to learn more, I'd recommend exploring [Practical Deep Learning for Coders](https://course.fast.ai/) by fast.ai. It describes itself as: diff --git a/docs/examples/08-extracting-features-from-text.md b/docs/examples/08-extracting-features-from-text.md index c4d91a1..07d5246 100644 --- a/docs/examples/08-extracting-features-from-text.md +++ b/docs/examples/08-extracting-features-from-text.md @@ -1,6 +1,6 @@ # 8. Extracting features from text -[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/08-extracting-features-from-text.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/08-extracting-features-from-text.ipynb) +[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/08-extracting-features-from-text.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/08-extracting-features-from-text.ipynb) In the last notebook, we saw that using a pre-trained network allowed us to extract features from images, and train a classifier for new categories on top of those features. We can do the same thing with text, using a pre-trained network to extract features from text. In this notebook, we'll use those features the visualise the similarities and differences between works in the collection, and try to find clusters of related material. diff --git a/docs/examples/09-building-a-recommender-system-for-subjects.md b/docs/examples/09-building-a-recommender-system-for-subjects.md index 099da36..6e566b1 100644 --- a/docs/examples/09-building-a-recommender-system-for-subjects.md +++ b/docs/examples/09-building-a-recommender-system-for-subjects.md @@ -1,6 +1,6 @@ # 9. Building a recommender system for subjects -[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/09-building-a-recommender-system-for-subjects.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/main/notebooks/09-building-a-recommender-system-for-subjects.ipynb) +[View on GitHub](https://github.com/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/09-building-a-recommender-system-for-subjects.ipynb) | [Run in Google Colab](https://colab.research.google.com/github/wellcomecollection/developers.wellcomecollection.org/blob/dependabot/npm_and_yarn/http-cache-semantics-4.1.1/notebooks/09-building-a-recommender-system-for-subjects.ipynb) Finally, we'll consider building a recommender system using data from Wellcome Collection. Thes machine learning models work slightly differently to the ones we've seen so far. Rather than being trained to predict a single value, they're trained to predict a whole matrix of interactions between two kinds of entity.