TC. LinMastering LeetCode (From a Software Engineer Perspective)Thanks for clicking in! I must admit, the title of this article might seem a bit “click-baity.” Wait, don’t leave just yet — I promise I…Jul 20Jul 20
TC. LinNLP back to basics: Text Representation ModelLarge Language Model is hitting the entire world, however, without understanding the basics, it’d be hard to step into the field of being a…Feb 26Feb 26
TC. LinML Feature Engineering: Efficiently Finding Feature CrossesThis article continues from the previous topic: Feature Crosses.Dec 17, 2023Dec 17, 2023
TC. LinML Feature Engineering: Feature CrossesWhen training an ML model, there are times when data features have complex relationships, making it harder for the model to learn the…Nov 19, 2023Nov 19, 2023
TC. LinFeature Normalization: The essential step in Machine Learning when dealing with numbers.Imagine that you are training a Machine Learning model that analyzes how the height and weight of a person affect health.Oct 22, 2023Oct 22, 2023
TC. LinML Feature Engineering: Dealing with Categorical FeaturesMost of the algorithms in traditional ML, namely, algorithms based on statistical equations, work best with numerical values. However…Sep 27, 2023Sep 27, 2023
TC. LinForward Propagation: The Neural Network PredictionsThis article continues from Neural Network Architecture: Stepping into Deep Learning.Sep 21, 2023Sep 21, 2023
TC. LinNeural Network Architecture: Stepping into Deep Learning (Part 3)This article continues from: Logistic Regression: The Gradient Descent.Sep 4, 2023Sep 4, 2023
TC. LinLogistic Regression: The Gradient DescentThis article continues from Logistic Regression — The One Layer Neural Network.Aug 19, 2023Aug 19, 2023
TC. LinLogistic Regression: The One-Layer Neural Network (Part 1)Fun fact: Logistic regression can be considered a One-Layer Neural Network!Aug 15, 2023Aug 15, 2023