Introduction to LLM
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
5.2 Compute Resources and Cost
A preview from Chapter 5.2: Learn why LLMs demand massive compute power, what drives cost, and practical strategies to optimize performance and sustainability.
2024-09-30
3.3 Fine-Tuning and Transfer Learning for LLMs: Efficient Techniques Explained
Learn how fine-tuning and transfer learning techniques can adapt pre-trained Large Language Models (LLMs) to specific tasks efficiently, saving time and resources while improving accuracy.
2024-09-14
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
3.1 LLM Training: Dataset Selection and Preprocessing Techniques
Learn about dataset selection and preprocessing techniques for training Large Language Models (LLMs). Explore steps like noise removal, tokenization, normalization, and data balancing for optimized model performance.
2024-09-12
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
1.3 Differences Between Large Language Models (LLMs) and Traditional Machine Learning
Understand the key differences between Large Language Models (LLMs) and traditional machine learning models. Explore how LLMs utilize transformer architecture, offer scalability, and leverage transfer learning for versatile NLP tasks.
2024-09-05