This post reviews all that is needed to understand how GPU executes code. The assumption is that you know the basics of computer architecture to grasp this concept fully. The story begins with Flynn’s taxonomy; the single instruction multiple data (SIMD) processor is scrutinized next. Then, x86 instructions for doing…

This post unravels these three words that may make life a little hard for newcomers to the deep learning world. Gradient Descent The story begins with the gradient descent optimization algorithm that trains neural networks. This algorithm changes the parameters based on the training set. It has three variants. The first one…

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. …

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. For example, the following figure shows the concept of a clean signal…

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. …