Today · Sunday, November 8, 2026

Random Forest: An Ensemble Learning Method

How random forests turn many unstable decision trees into one reliable model: bagging, random features, out-of-bag scores and working scikit-learn code.

AI & MLMachine Learning Algorithms13 min read

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Auto Scaling VMs on AWS, GCP, and Azure

How Auto Scaling groups, managed instance groups and Virtual Machine Scale Sets add and remove VMs, and how to tune them so scaling is fast and calm.

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Conditional GANs: Controlling the Output

How conditional GANs let you choose what the generator makes: label inputs, AC-GAN, the projection discriminator, conditional batch norm and pix2pix, with PyTorch code.

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Decision Trees: Making Decisions Using Data

How decision trees learn yes-or-no questions from data: Gini and entropy, overfitting and pruning, regression trees, and working scikit-learn code.

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Pricing Models: On-Demand, Reserved, Savings Plans, and Spot

How on-demand, reservations, savings plans, committed use discounts and Spot pricing work on AWS, GCP and Azure, and how to combine them safely.

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Training GANs: Tips, Tricks, and Common Pitfalls

Why GAN training breaks, how to read the signs, and the fixes that work: a solid DCGAN baseline, loss choices, spectral norm, R1, EMA and a debugging checklist.

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Logistic Regression: Understanding Probabilities

How logistic regression turns a weighted sum into a probability: the sigmoid, log loss, odds ratios, thresholds, imbalance and scikit-learn code.

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