Transformers

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

Complete guide to setting up a robust development environment for LLM fine-tuning, covering hardware requirements, software installation, data preparation workflows, and optimization techniques.

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.

Master parameter-efficient fine-tuning techniques with LoRA and QLoRA to customize large language models using minimal computational resources while maintaining high performance.

How Vision Transformers challenged CNNs by treating images like sentences - breaking them into patches and using attention to understand spatial relationships.

A conceptual deep-dive into the Transformer architecture that revolutionized AI. Learn the intuition behind attention, why it works, and how it powers modern language models like GPT and BERT.