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20 May 2026

Feature Engineering for Machine Learning: Practical Techniques

The often-overlooked craft of transforming raw data into informative features — covering encoding, scaling, missing values, interactions, and selection.

Feature Engineering Machine Learning Scikit-learn Data Science

6 May 2026

Diffusion Models Explained: The Math Behind Stable Diffusion

How denoising diffusion probabilistic models learn to generate images by reversing a gradual noising process — explained from the ground up.

Generative AI Diffusion Models Deep Learning Computer Vision

22 April 2026

Time Series Anomaly Detection with LSTMs

How to build a robust anomaly detection pipeline for multivariate sensor data using LSTM autoencoders in PyTorch, with practical tips on thresholding and deployment.

Time Series LSTM Anomaly Detection PyTorch MLOps

8 April 2026

Convolutional Neural Networks: How Machines See

A ground-up explanation of CNNs — convolution, pooling, receptive fields, and the architectural choices that made deep learning work for images.

Computer Vision CNN Deep Learning PyTorch

25 March 2026

Gradient Descent Optimizers: From SGD to Adam

A practical guide to the most widely used optimization algorithms in deep learning — what they compute, why they differ, and when to use each one.

Deep Learning Optimization PyTorch Training

10 March 2026

Understanding Transformers: The Architecture Behind Modern NLP

A deep dive into the self-attention mechanism and how transformers replaced recurrent networks to become the foundation of modern language models.

NLP Deep Learning Transformers PyTorch