LLM Basics – Fundamentals of Large Language Models is an introductory training program that explores how Large Language Models (LLMs) work, how they are developed, and how they can be applied to natural language processing tasks.

Throughout the course, participants examine the fundamental concepts behind LLMs, learn how language models can be built with PyTorch, and explore their use for inference in language-related applications.

The program reviews the historical development of large language models and their position within the broader Artificial Intelligence (AI) and Generative AI landscape. It also introduces neural-network-based language modeling and examines how Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer architectures are applied to natural language processing.

By combining essential theory with practical exercises, the training helps participants become familiar with language model architectures and their use in AI-powered applications.

The LLM Basics course aims to introduce participants to the essential concepts of large language models and demonstrate how these models can be used for inference when developing AI-powered applications.

Through practical activities guided by an experienced instructor, participants will work toward the following objectives:

Understand the fundamental principles of Natural Language Processing (NLP)
Apply text preprocessing and tokenization methods with NLTK
Explain word embeddings and the historical development of language models
Work with RNNs and LSTMs to process sequential data
Describe Transformer architectures and use important models such as BERT and GPT
Recognize the risks and limitations associated with large language models
Apply pre-trained models available through Hugging Face to NLP tasks
Understand the fundamental concepts and architecture of Retrieval-Augmented Generation (RAG) systems

For corporate groups, we can organize this training as an on-site private group.

Prerequisites

To follow the technical concepts and practical exercises effectively, participants should have the following knowledge and experience:

Proficiency in Python programming
Familiarity with data analysis using the Pandas library

Who Should Attend

LLM Basics is designed for individuals who want to understand natural language processing technologies and the fundamental principles behind large language models.

The course is particularly suitable for:

AI and Machine Learning Enthusiasts: Individuals interested in exploring Natural Language Processing (NLP) and Large Language Models (LLMs).
Data Scientists and Data Engineers: Professionals seeking to apply LLMs to inference and model fine-tuning tasks.
Software Developers: Developers looking to gain introductory hands-on experience with NLP libraries and large language models.
Students and Other Professionals: Learners who want to understand Transformer architectures and their role in powering modern AI models.

What You Will Learn

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

Training Outline

1. Introduction to Natural Language Processing (NLP)

This module introduces the core concepts of natural language processing, methods for preparing text data, and the development of language modeling approaches. Participants explore the progression from traditional NLP techniques to neural-network-based language models.

Topics Covered:

What is Natural Language Processing (NLP)?
NLP fundamentals: Text preprocessing and tokenization
NLP fundamentals: Word embedding techniques
Introduction to traditional NLP libraries
A brief history of language modeling
Using PyTorch and Hugging Face for text preprocessing
Neural networks and processing text data
Developing language models with RNNs and LSTMs

2. Transformers and Large Language Models

This module examines the fundamental characteristics of Transformer architectures and their relationship to large language models. It covers inference with pre-trained models, Generative AI applications, model fine-tuning, and the principles of RAG systems.

Topics Covered:

Introduction to Transformer architectures
Performing inference with Hugging Face Transformers
Large Language Models (LLMs) and Generative Artificial Intelligence
Overview of available large language model options
Fine-tuning GPT models
Aligning large language models with human values
Retrieval-Augmented Generation (RAG) systems

Why Choose Us

Leading UK-based global training provider since 1995.

Experience LLM Basics through Bilginç IT Academy's live and interactive virtual classroom environment. Join a Public course as an individual delegate or arrange a dedicated Private / In-house online training program exclusively for your organization.

Delivery Method: Online Instructor-Led
Participation Model: Public / Private (In-house)
Live and Interactive Training: Connect with your instructor in real time and actively participate through discussions, Q&A sessions, practical exercises, and group activities.
Flexible Participation: Join the training from your home, office, or any location with a suitable internet connection.
Expert Trainer Network: Learn from experienced trainers with strong industry backgrounds and practical field expertise.
Over 30 Years of Training Expertise: Benefit from Bilginç IT Academy's professional training experience since 1995.
Worldwide Access: Join our live virtual classrooms from anywhere in the world or arrange a dedicated online training program for your organization.

Experience LLM Basics in a professional face-to-face classroom environment. Classroom Based training can be delivered as a Public course open to individual delegates or as a dedicated Private / In-house class exclusively for your organization.

Delivery Method: Classroom Based
Participation Model: Public / Private (In-house)
Face-to-Face Learning: Interact directly with your instructor and fellow delegates in an engaging classroom environment.
Experienced Trainers: Learn from specialists with extensive industry experience and practical real-world knowledge.
Professional Training Environment: Attend training in comfortable, well-equipped classrooms designed to support effective learning.
Practical Learning: Depending on the course, reinforce your knowledge through hands-on exercises, scenarios, case studies, and instructor-led activities.

Arrange LLM Basics as a dedicated Onsite training program for your organization. Bilginç IT Academy trainers can deliver the training at your office or another location of your choice, with the program planned around your team's requirements and business objectives.

Delivery Method: Onsite
Participation Model: Private (In-house)
Training at Your Preferred Location: Organize the training at your company's office or another location selected by your organization.
Tailored Course Content: Adapt the training program to your projects, team structure, existing skill levels, and specific business requirements.
Team-Focused Learning: Develop your team around a shared knowledge base while strengthening internal collaboration and knowledge transfer.
Flexible Scheduling: Plan the training dates, location, and program according to your organization's operational requirements.
Worldwide Onsite Delivery: Bilginç IT Academy trainers can travel internationally to deliver dedicated training programs at your preferred location.