April 2024
Intermediate to advanced
398 pages
11h 1m
Polish
book
Uczenie maszynowe w Pythonie. Receptury. Od przygotowania danych do deep learningu.
by Kyle Gallatin, Chris Albon
Content preview from Uczenie maszynowe w Pythonie. Receptury. Od przygotowania danych do deep learningu.
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Rozdział 9. Redukcja wymiarowości za pomocą wyodrębniania cech
9.0. Wprowadzenie
Bardzo często masz dostęp do tysięcy, a nawet setek tysięcy cech. Na przykład w rozdziale 8. zostało przeprowadzone przekształcenie kolorowego obrazu o rozdzielczości 256×256 pikseli na 196 608 cech. Co więcej, każdy z tych pikseli może mieć jedną z 256 możliwych wartości, więc ostateczna liczba różnych konfiguracji wynosi 256196608. Taka ilość danych jest problematyczna dla wielu algorytmów uczenia maszynowego, ponieważ w praktyce nigdy nie będziemy w stanie zebrać wystarczającej liczby obserwacji. Dlatego też używane algorytmy nie będą miały wystarczającej ilości danych do prawidłowego funkcjonowania. Nawet w przypadku bardziej tabelarycznych, strukturyzowanych ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.