Spherical, Diagonal & Full Covariance | 3 Ways of defining a Multivariate Normal distribution скачать в хорошем качестве

Spherical, Diagonal & Full Covariance | 3 Ways of defining a Multivariate Normal distribution

The number of parameters in a Multivariate Normal scales quadratically. How can we reduce it? What are the implications. Let's look at examples in TensorFlow Probability. Here are the notes: https://raw.githubusercontent.com/Cey... Due to the covariance matrix, the number of parameters in a Multivariate Normal scales quadratically with the number of dimensions. For many applications, it is sufficient to define fewer parameters. In this video, we will look at the standard ways. ------- 📝 : Check out the GitHub Repository of the channel, where I upload all the handwritten notes and source-code files (contributions are very welcome): https://github.com/Ceyron/machine-lea... 📢 : Follow me on LinkedIn or Twitter for updates on the channel and other cool Machine Learning & Simulation stuff:   / felix-koehler   and   / felix_m_koehler   💸 : If you want to support my work on the channel, you can become a Patreon here:   / mlsim   ------- Timestamps: 00:00 Introduction 01:23 Overview 02:09 Full Covariance 04:27 TFP: Full Covariance 06:21 Diagonal Covariance 11:09 TFP: Diagonal Covariance 12:20 Spherical/Isotropical Covariance 16:15 TFP: Spherical/Isotropical Covariance 20:56 Outro