Introduction to LLM

Total of 16 articles available. Currently on page 1 of 1.

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.
2024-11-01

7.3 Integrating Multimodal Models

A preview from Chapter 7.3: Discover how multimodal models fuse text, images, audio, and video to unlock richer AI capabilities beyond text-only LLMs.
2024-10-09

7.1 The Evolution of Large-Scale Models

A preview from Chapter 7.1: Explore how LLMs have scaled from billions to trillions of parameters, the gains in performance, and the rising technical and ethical challenges.
2024-10-07

6.2 Simple Python Experiments with LLMs

A preview from Chapter 6.2: Learn how to run large language models with Hugging Face, OpenAI, Google Cloud, and Azure using just Python and a few lines of code.
2024-10-05

6.1 Introducing Open-Source Tools and APIs

A preview from Chapter 6.1: Explore Hugging Face, OpenAI, Google Cloud Vertex AI, and Azure Cognitive Services—leading tools to bring LLMs into your projects.
2024-10-04

6.0 Hands-On with LLMs

A preview from Chapter 6: Learn how to run large language models yourself with open-source libraries, cloud APIs, and Python—making LLMs accessible to everyone.
2024-10-02

5.0 Pitfalls & Best Practices When Using LLMs

Discover the hidden risks of large language models—bias, cost, and latency—and learn best practices for deploying LLMs responsibly.
2024-09-28

4.4 How LLMs Write Code: The Rise of AI-Powered Programming Assistants

Explore how large language models (LLMs) generate and complete code from natural-language prompts, and what it means for the future of software development.
2024-09-27

4.3 LLMs in Translation and Summarization: Enhancing Multilingual Communication

Learn how Large Language Models (LLMs) leverage Transformer architectures for accurate translation and summarization, improving efficiency in business, media, and education.
2024-09-18

4.1 Exploring LLM Text Generation: Applications, Use Cases, and Future Trends

Learn how Large Language Models (LLMs) are applied in text generation for content creation, email drafting, creative writing, and chatbots. Discover the mechanics behind text generation and its real-world applications.
2024-09-16

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

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

1.2 The Role of Large Language Models (LLMs) in Natural Language Processing (NLP)

Discover the impact of Large Language Models (LLMs) on natural language processing tasks. Learn how LLMs excel in text generation, question answering, translation, summarization, and even code generation.
2024-09-04

1.0 What is an LLM? A Guide to Large Language Models in NLP

Discover the basics of Large Language Models (LLMs) in natural language processing (NLP). Learn how LLMs like GPT and BERT are trained, their roles, and how they differ from traditional machine learning models.
2024-09-02

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

Category

Tags

Search History

Authors

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.