Linear

Part 2: Explaining the fundamental of linear algebra: matrix operations, and their propertiesPhoto by Pietro De Grandi on UnsplashThe previous article introduced what linear equations and linear systems are and how linear systems could be rewritten into the form of matrices. Therefore, it is important to know different operations on matrices and corresponding properties.Matrix is a rectangular array of numbers with m rows and n columns where m > 1 and n > 1. A vector is a matrix with a single row (row vector) or a…

Machine learning modeling using linear regression in Pythoncrystal ball, by DALL.E 2Are you familiar with Occam’s Razor? I remember a mention of it in the Big Bang Theory TV series! The idea behind Occam’s Razor is that all other things being equal, the simplest explanation for a phenomenon is more likely to be true than a more complex one (i.e. the simplest solution is almost always the best solution). I’d like to think that Occam’s Razor of predictive modeling in machine learning is linear regression, which is almost…

A gentle refresher on essential concepts and operations for data sciencePhoto by Vashishtha Jogi on UnsplashIntroductionRegardless of whether you’re fitting a simple regression model or a convolutional neural net, linear algebra plays a significant role in making those computations efficient. While most are likely familiar with its use in geometry — where it’s used to define objects such as lines, and planes, along with their transformations (like rotations and translations) — linear algebra is foundational across the…

A step-by-step tutorial on building a linear regression model with PySpark codePhoto by eskay lim on UnsplashPySpark is a Python API for Apache Spark. It allows us to code in a high level coding language while reaping the benefits of distributed computing. With in-memory computation, distributed processing using parallelize, and native machine learning libraries, we unlock great data processing efficiency that is essential for data scaling.This tutorial will go step-by-step on how to create a PySpark linear regression…

Linear Regression from Scratch: Detailed ExplanationImage By AuthorMotivationMachine learning is a process by which machines can learn from the data and take a rational decision on new data without explicit programming. The base of these models is mathematics and statistics. Linear regression is one of the simple and widely used regression algorithms. The regression algorithms predict continuous values.For example, we want to predict the price, age, weight, etc. Such types of values are not countable. That’s why these are…

Farm with corn. Still life by Dall-E 2.Tackling a wide range of optimization problemsLinear programming is a powerful tool. It has applications in a wide range of fields, including engineering, finance, and operations research. Linear programming has been used to solve problems as diverse as scheduling airline flights and designing manufacturing processes. In this blog post, we will explore the basics of linear programming and how it can be used to solve practical problems.Linear programming (LP) is a mathematical…

Predict the Price of Diamonds Using Linear RegressionPhoto by Bas van den Eijkhof on UnsplashLinear regression is a powerful but relatively simple tool that can be used to understand the relationship between variables. This tutorial will explore the fundamentals of linear regression in a beginner-friendly way. By the end of this tutorial, you will have a solid understanding of linear regression and how to implement it using real-world data.Linear regression, a statistical method first used in 1877, predicts the value of a…

Starting from using matrix and vector for data representationLinear Algebra for Machine Learning (image by author)Truth be told, the role of linear algebra in machine learning has been perplexing me, as mostly we learn these concepts (e.g. vector, matrix) in a math background while discarding their applications in the machine learning context. In fact, linear algebra has several foundational use cases in machine learning, including data representation, dimensionality reduction and vector embedding. Starting from…

Compensating for trend in exponential smoothing modelsPhoto by Jeremy Thomas on UnsplashIn my previous post, we introduced the idea of exponential smoothing for building forecasting models. The gist of exponential smoothing is to put more weight on recent observations and less weight, exponentially, on more historical ones.The first model we introduced was simple exponential smoothing. The ‘simple’ part refers to the model not taking into account trend or seasonality, and only forecasting the level. This leads to this…

Probabilistic deep learningThis article belongs to the series “Probabilistic Deep Learning”. This weekly series covers probabilistic approaches to deep learning. The main goal is to extend deep learning models to quantify uncertainty, i.e. know what they do not know.We develop our models using TensorFlow and TensorFlow Probability. TensorFlow Probability is a Python library built on top of TensorFlow. We are going to start with the basic objects that we can find in TensorFlow Probability and understand how can we…