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Discover how the RBF (Radial Basis Function) kernel works by implicitly mapping data into an infinite-dimensional space to solve ...

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Gaussian process regression (GPR) is a probabilistic approach to making predictions. GPRs are easy to implement, flexible, and ...

SVM can only produce linear boundaries between classes by default, which not enough for most machine learning applications.

In this video, we explore Gaussian processes, which are probabilistic models that define distributions over functions, allowing us ...

Briefly explains the Gaussian distribution and why it is so important. * If you would like to support me to make these videos, you ...

Image filters make most people think of Instagram or Camera Phone apps, but what's really going on at pixel level? Image Analyst ...

All about Gaussian Processes and how we can use them for regression. RBF Kernel ...

This video is part of an online course, Model Building and Validation. Check out the course here: ...

This video is part of the Udacity course "Computational Photography". Watch the full course at ...

Learn how kernel density estimation (KDE) works with a simple exam score example. We'll explore how statisticians use kernels, ...

Smoothing of noisy sensor readings through convolution with a Gaussian kernel.

A visual trick to compute the sum of two normally distributed variables. 3b1b mailing list: https://3blue1brown.substack.com/ Help ...

The *most powerful* kernel in all the land. SVM Kernels Video: https://youtu.be/OKFMZQyDROI My Patreon ...

All about Kernel Density Estimation (KDE) in data science. Fish Icon: ...

Some parametric methods, like polynomial regression and Support Vector Machines stand out as being very versatile. This is due ...

Full video list and slides: https://www.kamperh.com/data414/

Code Review: Create a two-dimensional Gaussian kernel Helpful? Please support me on Patreon: ...

I cover two methods for nonparametric regression: the binned scatterplot and the Nadaraya-Watson kernel regression estimator.

... Example of kernels 54:13 Kernel matrix 59:16 Gaussian kernel 59:39 The gaussian kernel 1:11:57 Dual form 1:13:35 Examples ...