LLM Basics Eğitimi

Who Should Attend?

• Proficiency in Python programming
• Familiarity with data analysis using Pandas

Working with an engaging, hands-on learning environment, and guided by an expert instructor, students will learn the basics of Large Language Models (LLMs) and how to use them for inference to build AI powered applications.

• Understand the basics of Natural Language Processing
• Implement text preprocessing and tokenization techniques using NLTK
• Explain word embeddings and the evolution of language models
• Use RNNs and LSTMs for handling sequential data
• Describe what transformers are and use key models like BERT and GPT
• Understand the risks and limitations of LLMs
• Use pre-trained models from Hugging Face to implement NLP tasks
• Understand the basics of Retrieval-Augmented Generation (RAG) systems

1. Introduction to NLP
   • What is NLP?
   • NLP Basics: Text Preprocessing and Tokenization
   • NLP Basics: Word Embeddings
   • Introducing Traditional NLP Libraries
   • A brief history of modeling language
   • Introducing PyTorch and HuggingFace for Text Preprocessing
   • Neural Networks and Text Data
   • Building Language Models using RNNs and LSTMs
2. Transformers and LLMs
   • Introduction to Transformers
   • Using Hugging Face’s Transformers for inference
   • LLMs and Generative AI
   • Current LLM Options
   • Fine tuning GPT
   • Aligning LLMs with Human Values
   • Retrieval-Augmented Generation (RAG) Systems