Machine-learning

Deep dive into reward modeling - the critical first step in RLHF that teaches AI systems to predict and optimize for human preferences through comparative learning and preference ranking.

Comprehensive guide to supervised fine-tuning of Large Language Models, covering data preparation, training implementation, hyperparameter optimization, and evaluation strategies with practical code examples.

Comprehensive introduction to Large Language Model fine-tuning, covering theoretical foundations, key concepts, and when to choose different fine-tuning approaches for your specific use case.

Understanding image augmentation - why it's crucial for computer vision success, what techniques work best, and how to apply them effectively to build robust models with limited data.

How ChatGPT works under the hood - from predicting the next word to engaging in human-like conversations. Understanding the magic behind large language models.

Understanding XGBoost - the extreme gradient boosting algorithm that dominates machine learning competitions. Learn how it works, why it's so effective, and when to use it.