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Machine Learning Hub
Practical guides on machine learning, model tuning and fine-tuning large language models, explained step by step.
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Machine Learning Basics
Regression, classification and the core ideas behind them, with worked examples.
Model Optimization & MLOps
Hyperparameter tuning, regularization and how to get a model ready for real use.
NLP & Fine-Tuning LLMs
How large language models are chosen, fine-tuned and aligned, from LoRA to RLHF.
Latest machine learning guides
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Activation Functions Explained: The Tiny Switch That Makes Neural Networks Smart
Why can’t a neural network work without activation functions? Meet sigmoid, tanh, ReLU, GELU and softmax in plain English, with…
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Complete Guide to Fine-Tuning LLMs: From Prompt-Tuning to RLHF for Smarter AI
LoRA, QLoRA, prompt-tuning or RLHF? Compare every LLM fine-tuning method, see the trade-offs and pick the right one for your…
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Choosing the Right Base Model for Fine-Tuning LLMs: Strategy Before Execution
Picking the wrong base model can sink your fine-tuning project. Compare Llama, Mistral, Qwen and more on context, licence, cost…
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History of Programming: From Ada Lovelace to AI
From the abacus to Ada Lovelace to AI: the milestones, people and breakthroughs that shaped programming, told in a quick,…
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How Logistic Regression Works: Beginner to Pro Guide
Logistic regression made simple: sigmoid, probabilities and maximum likelihood, with a real diabetes prediction example anyone can follow.
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Linear Regression and Hyperparameter Tuning: Complete Guide
Underfitting or overfitting? Learn how to tune linear regression in scikit-learn and when Ridge or Lasso will beat plain linear…
Coming soon: deep learning, data visualization and AI tools guides.
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