Introduction to LLM

Total of 11 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.

2025-09-01

7.4 Data Ethics and Bias in Large Language Models

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.

2024-10-09

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

7.2 Resource-Efficient Training

A preview from Chapter 7.2: Learn how techniques like distillation, quantization, distributed training, and data efficiency make LLMs faster, cheaper, and greener.

2024-10-08

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

6.1 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

5.3 Real-Time Deployment Challenges

A preview from Chapter 5.3: Explore latency, scalability, and optimization techniques for deploying large language models in real-time applications.

2024-10-01

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

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

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

Search History

Aufgabenverwaltung 1267 interface do usuário 1227 AI-powered solutions 1201 améliorations 1194 colaboración 1187 2FA 1186 language support 1172 atualizações 1164 búsqueda de tareas 1163 modèles de tâches 1159 ActionBridge 1145 Produktivität 1141 Aufgaben suchen 1135 interfaz de usuario 1131 Version 1.1.0 1118 joindre des fichiers 1113 anexar arquivos 1096 new features 1093 Transformer 1090 Aufgabenmanagement 1081 busca de tarefas 1075 interface utilisateur 1070 Teamaufgaben 1065 feedback automation 1059 Two-Factor Authentication 1047 modelos de tarefas 1043 CS data analysis 1024 customer data 1024 Google Maps review integration 1019 mentions feature 978

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.