Category: machine learning

Pre-requisite: some understanding of reinforcement learning. If not, you can start from Reinforcement Learning Primer Goal Let’s analyze this in the classic Multi-Armed Bandit problem using

Reinforcement learning is going to be “the next big thing” in machine learning after 2022, so let’s understand some basic on how it works. Agent:

The positional encoding of transformer was a detail added in Attention Is All You Need. When I first saw this, I thought “why is the

The KL (Kullback–Leibler) Divergence and JS (Jensen-Shanon) Divergence are ways to measure the distance (similarity) between two distributions P and Q. I will try to

Background In 2022, if you are not new to NLP (Natural Language Processing), you should have heard of BERT (Bidirectional Encoder Representations from Transforms). It’s

Goal Gradient descent seems to work fine for finding the local maxima and minima of a function, and Lagrange Multiplier helps to find the local

What are eigenvalues and eigenvectors? We have to answer both concept together since they are closely related? Given a square matrix A If we find

Why is scaling important? Because the incoming traffic will grow overtime and model will become more complex Scaling dimension Data volume Model parameters Types of

What is serving? Provide access to end users Provide service/app for interaction In ML workflow Batch inference Online Inference regularly retrained inference with the latest

Model fairness is about assess how the output of the model can impact different subgroup of the population. Tool: Tensorflow Fairness Indicators For binary and