Large language models, the essentials at a glance!

This course covers machine learning theory and practical examples based on <Machine Learning, Just the Essentials—Fast!> (Insight, 2025).

This course covers the GitHub notebooks and bonus content for <Building an LLM from Scratch> (Gilbut, 2025). GitHub: https://github.com/rickiepark/llm-from-scratch/ <Building an LLM from Scratch> is the Korean translation of the bestseller <Build a Large Langauge Model (from Scratch)> (Manning, 2024) written by Sebastian Raschka. Starting from scratch and building a complete version of OpenAI’s GPT-2 model, this book provides a way to learn how large language models work and how to use them.

"Your first step to creating a customized LLM with LoRA-based lightweight fine-tuning!" This is an introductory hands-on course designed so that even those new to LLMs can easily follow along. We minimize complex theory and guide you step-by-step through the entire process: loading the model → applying data → training → comparing results. In a short time, you'll directly experience the workflow of cutting-edge lightweight fine-tuning techniques like LoRA and QLoRA, and gain an intuitive understanding of "how LLM fine-tuning works." Even without extensive resources, experience the satisfaction of creating an LLM specialized for your domain!

You can enhance your development capabilities by deeply exploring the fundamental principles of large language models like ChatGPT and Claude. You will be able to learn a variety of information and development techniques that are not commonly encountered.

We will learn step by step from the basic concepts of LLM (Large Language Model) to how to fine-tune the Llama 2 model, a high-performance LLM, on the dataset of your choice.

You will learn about Transformers inside out by directly implementing everything from Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder-based MarianMT translation model, and the Vision Transformer in code.

Mastering difficult AI with just one single analogy?! A first step into AI principles, understanding through core concepts rather than just terminology.

This course will teach you the concept of the Google Gemini model and how to use the Gemini API, and create various AI applications using Streamlit.

You will learn about RAG, VectorDB, LangChain, OpenAI, sLLM, and fine-tuning, which encompass the most important AI-related technologies at this time.

The era of AI simply providing answers is over. Now is the age of LLM Agents that make decisions and take actions on their own. This course is an introductory lecture where you learn the core principles and structure of agents by implementing them yourself in just 90 minutes of hands-on practice. With minimal complex theory and a code-focused practical flow, you can directly experience "how AI makes decisions and uses tools." Beyond prompt engineering, let's take the first step into AI automation together.

Using AI has become easier, but model cards and papers are still difficult to read, even just a few lines. Although you can look up the meanings of individual words, it’s not clear how those words connect to one another. This course builds up around 160 essential terms that frequently appear in AI documents, in the easiest order to understand them, so that in each lesson you can accurately read a single sentence from an actual document.

You will build Android/iOS apps based on simple machine learning models and learn the fundamentals of machine learning, model conversion using TFLite, and the process of implementing Android and iOS applications.

We will explore the types of applications and web services that can be built using LLMs.

👉 This course is ideal for those who want to properly understand how AI changes and its limitations. Is AI finished once it’s created? This course explains how AI changes through learning and why the results can differ even with the same AI. It clearly explains the meaning of the learning process and the technical reasons why performance may improve or decline.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

Master the complete lifecycle of building modern Generative AI applications using Large Language Models, Retrieval-Augmented Generation, and Agentic AI systems. Learn to design enterprise-grade AI solutions from prompt engineering to deployment, combining LLMs with vector databases and external knowledge sources for production-ready applications.

From perceptrons to backpropagation, systematically learn the core mathematical theories of neural networks and implement them directly in R. Build a solid foundation in deep learning—including activation functions, gradient descent, and multilayer neural networks—while developing the ability to solve classification problems using real-world datasets.

Have you been getting stuck on linear algebra formulas every time you try to study AI? Instead of memorizing formulas, this lecture shows you visually how vectors and matrices actually move in space. By the end of the course, you will be able to interpret the mathematics behind neural networks, PCA, and SVD on your own.

Starting with the question, "If I were on the Titanic, would I have survived?", this course completes a full-stack project where you develop an AI model to predict survival probabilities based on real data and deploy it as a web service. You will gain hands-on experience in the entire process of AI and web development, from deep learning modeling using PyTorch to building a backend server with FastAPI and implementing a user interface with Next.js.