Machine Learning – ML

AI Machine Learning - ML Statistics
Gaussian Distribution Made Simple: A Beginner-Friendly Guide for Anomaly Detection
August 20, 2026
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A Gaussian distribution is also called: Gaussian Distribution = Normal Distribution = Bell-Shaped Distribution They all mean the same thing. 1. What Is a Gaussian Distribution? A Gaussian distribution describes data where: Most values are close to the average, while values become less common as we move farther away from the average. For example, imagine […]
AI LLM Machine Learning - ML
From SFT to GRPO: A Practical Guide to Modern LLM Fine-Tuning, Alignment, PEFT, LoRA and QLoRA
August 20, 2026
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Large Language Models do not become specialized, aligned, or reasoning-focused through a single training technique. When working with models such as Qwen, Llama, Mistral, or other open-weight LLMs, we encounter terms such as: SFT, DPO, PPO, GRPO, PEFT, LoRA, and QLoRA. At first, these can look like competing fine-tuning methods. They are not. Some define […]
AI Machine Learning - ML
TensorFlow Activation Functions
August 7, 2026
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The Most Important Memory Rule Memorize this: Hidden layers → ReLU Binary classification → Sigmoid Multiclass classification → Softmax Multilabel classification → Sigmoid Regression → Linear An even easier memory sentence is: ReLU inside, Sigmoid for yes/no, Softmax for one-of-many, Sigmoid for many-of-many, Linear for numbers. 1. ReLU: The Default Choice for Hidden Layers ReLU […]
AI Machine Learning - ML NLP
Which Activation Function Should You Use in NLP? A Practical Guide from Text Classification to LLM Text Generation
August 7, 2026
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Choosing the correct activation function in NLP can be confusing because different NLP problems produce very different kinds of outputs. A sentiment classifier, a spam detector, a named entity recognition model, and a large language model may all process text, but they do not necessarily use the same output activation. The easiest way to remember […]
AI Machine Learning - ML
How to Detect Overfitting, Underfitting, and Good Fitting from Machine Learning Graphs
August 7, 2026
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How to Detect Overfitting, Underfitting, and Good Fitting from Machine Learning Graphs
Training a machine learning model is not only about achieving high accuracy. A model can reach 99% training accuracy and still perform badly on new data. To evaluate a model professionally, you need to understand the relationship between: Training accuracy Validation accuracy Training loss Validation loss Test accuracy These metrics help determine whether the model […]