These are completely different methods in machine learning. The fact that they both have the letter K in their name is a coincidence. K-means is a clustering algorithm that tries to partition a set of points into K sets (clusters) such that the points in each cluster tend to be near each other. It is unsupervised because the points have no external classification. The typical k-means...
Predictor VS. Estimator quick note
There is much confusion for beginners in machine learning. One of the frequently asked questions is the difference between predictor vs. estimator. Let get some note: Different usage “Prediction” and “estimation” indeed are sometimes used interchangeably in non-technical writing and they seem to function similarly, but there is a sharp distinction between them in the...
Common Loss functions and their uses – quick note
Machines learn by means of a loss function which reflects how well a specific model performs with the given data. If predictions deviate too much from actual results, loss function would yield a very large value. Gradually, with function, parameters are modified accordingly to reduce the error in prediction. In this article, we will quickly review some common loss functions and their usage in the...
A.I in agriculture – Fruit Grading with Keras (part 2)
In part 1, we introduced fruit classification with pure python implementation. In this part, we will use the Keras library instead. What is Keras Keras is an open-sourceneural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation...
AI in agriculture: fruit grading (Part 1)
During a meet up last month, a friend told me about the current project on a farm in New Zealand. They want to build a system to grade their fruits and AI is the technology they are looking for. It inspired me to write about how machine learning can help in solving such a problem. Fig 1: Apple grading The grading task Given an apple, we need to sort it to correct category in three available...