In this article, we shall see how the Bayesian Kalman Filter can be used to predict positions of some moving particles / objects in 2D.



This article is inspired by a programming assignment from the coursera course Robotics Learning by University of Pennsylvania, where the goal was to implement a Kalman filter for ball tracking in 2D space. Some part of the problem description is taken from the assignment description.

The following…

The problem

Given a set of labeled images of  cats and dogs, a  machine learning model  is to be learnt and later it is to be used to classify a set of new images as cats or dogs. 

• The original dataset contains a huge number of images, only a few sample images are chosen (1100 labeled images for cat/dog as training…

The following problems appeared in the assignments in the Udacity course Deep Learning (by Google). The descriptions of the problems are taken from the assignments (continued from the last post).

Classifying the letters with notMNIST dataset with Deep Network

Here is how some sample images from the dataset look like:

Let’s try to get the best performance using a multi-layer model!…

• In this article, a mathematical model for the growth of a sunflower (shown below) will be described (reference: the video lectures of Prof. Jeffrey R Chesnov from Coursera Course on Fibonacci numbers).

  

• New florets are created close to center.

• Florets move radially out with constant speed as the sunflower grows.

• Each new…