Zero to AI: A non-technical, hype-free guide to prospering in the AI era by Nicolò Valigi & Gianluca Mauro

Author:Nicolò Valigi & Gianluca Mauro
Language: eng
Format: mobi, epub
Publisher: Manning Publications Co.
Published: 2020-05-09T22:00:00+00:00

Figure 6.5 Embeddings of three T-shirts represented on a 2-D plane. T-shirts A and B are similar, so their embeddings are close. T-shirt C is different from A and B, so it’s far from both.

Imagine now that a user clicks on the product page for T-shirt A and spends time looking at it, checking the available sizes, colors, and materials. We can interpret this as a manifestation of interest in that T-shirt’s style, and recommend another one that has an embedding close to it; for instance, T-shirt B. We’ll avoid suggesting T-shirt C, as we know that its embedding is far from that of the T-shirt that the user liked, and therefore it probably won’t match their taste.

This is again the magic of deep learning and of embeddings: neural networks found a way to “understand” images and transform them into a small set of numbers that actually make sense (for a computer, at least). If you think about it, images are not the only applications where you’ve seen the power of embeddings. In chapter 5, you saw how powerful embeddings can be when dealing with another complicated kind of data: text.

The same approach we described works with text as well. Suppose we are trying to suggest news articles to a user on a website, and we want to find a piece of news that is similar to the ones where our user spends most of his time. We can transform the words in each article in its embeddings and place our new vectorized articles in space, as shown in figure 6.6.

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