8.6 David Thompson (Part 6): Nonlinear Dimensionality Reduction: KPCA
Комментарии отсутствуют
Информация о видео
Другие видео канала
8.4 David Thompson (Part 4): Linear Dimensionality Reduction
UMAP Uniform Manifold Approximation and Projection for Dimension Reduction | SciPy 2018 |
The Kernel Trick - THE MATH YOU SHOULD KNOW!
StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
6. Singular Value Decomposition (SVD)
StatQuest: Principal Component Analysis (PCA), Step-by-Step
Feynman at Caltech - John Preskill and Kip Thorne - 5/11/2018
Kernel PCA with Python Codes
StatQuest: PCA in Python
Dick’s Tricks - Leonard Susskind - 5/11/2018
SVM10 The Kernel Trick (Part1: Basis Expansion)
Dimensionality Reduction - The Math of Intelligence #5
StatQuest: t-SNE, Clearly Explained
A Bluffer's Guide to Dimension Reduction - Leland McInnes
Data Analysis 6: Principal Component Analysis (PCA) - Computerphile
Nonlinear dimensionality reduction for faster kernel methods in machine learning - Christopher Musco
Principal Component Analysis (PCA)
Dimensionality Reduction and Clustering Tools for High Parameter Discovery
Lecture 9 on kernel methods: kernel PCA