Тёмный

t-distributed Stochastic Neighbor Embedding (t-SNE) | Dimensionality Reduction Techniques (4/5) 

DeepFindr
Подписаться 34 тыс.
Просмотров 6 тыс.
50% 1

Опубликовано:

 

3 окт 2024

Поделиться:

Ссылка:

Скачать:

Готовим ссылку...

Добавить в:

Мой плейлист
Посмотреть позже
Комментарии : 7   
@DeepFindr
@DeepFindr 7 месяцев назад
To try everything Brilliant has to offer-free-for a full 30 days, visit brilliant.org/DeepFindr​. The first 200 of you will get 20% off Brilliant’s annual premium subscription.
@王恺风
@王恺风 2 дня назад
I really enjoy this video! It is so concise, comprehensive and beautiful! And thanks a lot for so many useful links for further learning.
@chemicalengineeringfriends217
@chemicalengineeringfriends217 6 месяцев назад
Great videos! Looking forward to other parts :)
@clairenajjuuko7664
@clairenajjuuko7664 7 месяцев назад
Great video. looking forward to the UMAP video. Will you also be doing something on FAMD?
@DeepFindr
@DeepFindr 7 месяцев назад
Thanks! So far only UMAP is planned but maybe more methods will be added in the future :)
@lucapalese475
@lucapalese475 7 месяцев назад
Really nice! I will read those papers , I guess the backprop is more complex with the t-distribution
@DeepFindr
@DeepFindr 7 месяцев назад
Actually it should be easier because the distribution has an easier function
Далее
Лиса🦊 УЖЕ НА ВСЕХ ПЛОЩАДКАХ!
00:24
t-SNE Simply Explained
25:49
Просмотров 13 тыс.
Visualising High-Dimensional Data with t-SNE
21:17
Просмотров 9 тыс.
Why Does Diffusion Work Better than Auto-Regression?
20:18
StatQuest: t-SNE, Clearly Explained
11:48
Просмотров 467 тыс.
The moment we stopped understanding AI [AlexNet]
17:38
UMAP Dimension Reduction, Main Ideas!!!
18:52
Просмотров 108 тыс.