Introduction to LLM

Total of 8 articles available. Currently on page 1 of 1.

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

6.0 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

5.3 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

5.0 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

4.1 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

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

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

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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.