Software engineering, AI systems and technical paper reviews.
How I predicted California house prices with linear regression trained by gradient descent (SGDRegressor) — mutual-information feature selection, a scaling gotcha that breaks SGD, and an honest benchmark against KNN, a decision tree, and WEKA.
How I built a speech emotion recognition model in Python — parsing MFCC voice features, pruning them with mutual information, and benchmarking five classifiers (logistic regression, naïve Bayes, KNN, decision tree, MLP) in scikit-learn against WEKA.
What is logistic regression? Learn how this classification algorithm uses the sigmoid function, log loss, and gradient descent to predict probabilities and class labels.
What is multivariate (multiple) linear regression? Learn how this ML algorithm fits a hyperplane with one weight per feature plus a bias to predict values from many inputs.
What is univariate linear regression? Learn how this machine learning algorithm uses weights and bias to fit a line and predict values from one feature.
Artificial Intelligence didn’t start with ChatGPT. From its origins in the 1950s to today’s deep learning models, let’s explore what AI really is, how it learns from data, and why it’s not just computer “magic.”
I write about the backend×AI wedge — wins and failures included. Follow along, or say hi.