## Short Note on Matrix Differentials and Backpropagation

Mathematical notation is the convention that we all use to denote a concept in a concise mathematical formulation, yet sometimes there is more than one way t...

Mathematical notation is the convention that we all use to denote a concept in a concise mathematical formulation, yet sometimes there is more than one way t...

In Machine Learning, supervised problems can be categorized into regression or classification problems. The categorization is quite intuitive as the name ind...

We have heard enough about the great success of neural networks and how they are used in real problems. Today, I want to talk about how it was so successful ...

A Gaussian process is a non-parametric model which can represent a complex function using a growing set of data. Unlike a neural network, which can also lear...

In the previous post, we covered variational inference and how to derive update equations. In this post, we will go over a simple Gaussian Mixture Model with...

Statistical inference involves finding the right model and parameters that represent the distribution of observations well. Let $\mathbf{x}$ be the observati...

Python layer in Caffe can speed up development process Issue1703

Parametric Regression uses a predefined function form to fit the data best (i.e, we make an assumption about the distribution of data by implicitly modeling ...

Reading protobuf DB in python

Fast way to sample points on a triangular mesh

Caffe is one of the most popular open-source neural network frameworks. It is modular, clean, and fast. Extending it is tricky but not as difficult as extend...

Gradient propagation is the crucial method for training a neural network

Techniques for measuring two rotation matrices