Introduction to LLM

Total of 9 articles available. Currently on page 1 of 1.

4.0 Applications of LLMs: Text Generation, Question Answering, Translation, and Code Generation

Discover how Large Language Models (LLMs) are used across various NLP tasks, including text generation, question answering, translation, and code generation. Learn about their practical applications and benefits.
2024-09-15

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

2.3 Key LLM Models: BERT, GPT, and T5 Explained

Discover the main differences between BERT, GPT, and T5 in the realm of Large Language Models (LLMs). Learn about their unique features, applications, and how they contribute to various NLP tasks.
2024-09-10

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

1.1 Understanding Large Language Models (LLMs): Definition, Training, and Scalability Explained

Explore the fundamentals of Large Language Models (LLMs), including their structure, training techniques like pre-training and fine-tuning, and the importance of scalability. Discover how LLMs like GPT and BERT work to perform NLP tasks like text generation and translation.
2024-09-03

A Guide to LLMs (Large Language Models): Understanding the Foundations of Generative AI

Learn about large language models (LLMs), including GPT, BERT, and T5, their functionality, training processes, and practical applications in NLP. This guide provides insights for engineers interested in leveraging LLMs in various fields.
2024-09-01

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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.