Introduction to LLM

Total of 5 articles available. Currently on page 1 of 1.

1.3 Entropy and Information: Quantifying Uncertainty

A clear, intuitive exploration of entropy, information, and uncertainty in Large Language Models. Learn how information theory shapes next-token prediction, why entropy matters for creativity and coherence, and how cross-entropy connects probability to learning. This section concludes Chapter 1 and prepares readers for the conceptual foundations in Chapter 2.
2025-09-06

Understanding LLMs – A Mathematical Approach to the Engine Behind AI

A preview from Chapter 7.4: Discover why large language models inherit bias, the real-world risks, strategies for mitigation, and the growing role of AI governance.
2025-09-01

3.2 LLM Training Steps: Forward Propagation, Backward Propagation, and Optimization

Explore the key steps in training Large Language Models (LLMs), including initialization, forward propagation, loss calculation, backward propagation, and hyperparameter tuning. Learn how these processes help optimize model performance.
2024-09-13

3.0 How to Train Large Language Models (LLMs): Data Preparation, Steps, and Fine-Tuning

Learn the key techniques for training Large Language Models (LLMs), including data preprocessing, forward and backward propagation, fine-tuning, and transfer learning. Optimize your model’s performance with efficient training methods.
2024-09-11

2.2 Understanding the Attention Mechanism in Large Language Models (LLMs)

Learn about the core attention mechanism that powers Large Language Models (LLMs). Discover the concepts of self-attention, scaled dot-product attention, and multi-head attention, and how they contribute to NLP tasks.
2024-09-09

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SHO

CTO of Receipt Roller Inc., he builds innovative AI solutions and writes to make large language models more understandable, sharing both practical uses and behind-the-scenes insights.