– – – coming soon – – –
Course Description
Artificial intelligence has become an extremely important area for IT professionals and engineers in the past 10-20 years with the scientific breakthroughs and practical applications of deep learning and more recently of generative AI systems, especially with its Large Language Model (LLM) variant such as OpenAI’s ChatGPT and Google’s Bard. Due to its importance and impact on every aspect of our lives, understanding the concepts, functionalities and practical usage of AI systems is quickly becoming essential for all IT and other technical professionals as well as for managers with technical background.
This training focuses on Large Language Models (LLMs) and teaches participants the following topics (preliminary version):
- Short history and general methods of Natural Language Processing (NLP) before Transformers
- Basic understanding of Transformers, the core technology of LLLMs
- Introducing the Hugging Face Ecosystem, the preferred way of working with open-source LLMs
- Using LLMs for Text Classification
- Using LLMs for Named Entity Recognition
- Using LLMs for Text Generation
- Using LLMs for Summarization
- Using LLMs for Question Answering
- LLM fine tuning with domain specific data
- Reinforcement Learning with Human feedback (RLHF)
Besides gaining a basic understanding of the theory of Large Language Models (LLMs) models, students will also make extensive lab exercises using the Python based TensorFlow or PyTorch deep learning framework with the Hugging Face ecosystem.
This training is part of the AI portfolio of Component Soft which explores essential AI topics, such as ChatGPT and GPT API Prompt Engineering for Developers, Deep Learning Basics, Large Language Models (LLM) Basics, and Generative AI Basics.
Course Length: 24 training hours
Structure: 50% theory, 50% hands on lab exercises
Target audience: All types of IT, telecom, and other technical professionals as well as managers with technical background who want to understand the basic concepts, functionalities and practical usage of Large Language Models (LLMs).
Prerequisites: Basic understanding of AI, Machine Learning and Deep Learning concepts. Basic Python programming skills. Some experience in using TensorFlow or PyTorch deep-learning frameworks.
Suggested preliminary course: AI-202 Deep Learning Basics