This article is an extended one gathering all the needed information to understand MPS. This article is like a timeline story that elaborates on required concepts to understand the goal. It starts with CPU-GPU difference, elaborates on the underutilization issue, CUDA streams, Hyper-Q, and finishes with MPS. If the reader…

This post concisely reviews the profiling concept and how to profile a deep learning model with TensorFlow. Why Profiling? Profiling a computer program aims to know more about its behaviour. By understanding the behaviour of a program, the developers can carry out optimizations resulting in higher performance. In addition, by profiling a…

Introduction This post reviews the principal component analysis (PCA) concept. PCA is a feature or dimensionality reduction technique. Features are the elements that we use their values for each sample of data we use for training. Consider the following example we want to have a machine learning (ML) model for predicting…

In this post, we look at regression’s intuition. Linear Regression Let’s consider the most straightforward scenario we saw as high schoolers. Assume that we want to find the line equation that goes through two points. The following example shows how we do it in linear algebra. But, when the number of data…

Noise, denoising, and filtering are very primary concepts in image processing. As machine learning got ubiquitous as a new programming paradigm, people need to know the basics of vision. The noise is any unwanted added to the data. …

Knowing the concept of Convolution became crucial for computer science people, especially data scientists when convolution neural networks significantly improved vision tasks. The convolution operator says to keep one function fixed, then move the other one over the fixed one and multiply them. …

This post summarizes the data clearing and emphasizes its importance. It gives you ideas about data cleaning through manual and automatic approaches like RANSAC, and finally, with morphology on images, which cleans binary images, finishing with concepts like normalization, standardization, and regularization. …

In this post, I review what machine learning is about starting from the data to the decision making. I think people should know how to use machine learning as a new programming paradigm. Note that linear algebra is the basis of this area. …

This post reviews basic linear algebra for introductory machine learning. Note: It will develop over time. Constructive comments are welcome. Introduction Linear algebra is a branch of math providing a way of compactly representing and operating on sets of linear equations. For example, consider the following set of equations: In…