<밑바닥부터 만들면서 배우는 LLM> 깃허브 + 보너스 콘텐츠 해설

This is a lecture covering machine learning theory and practical examples based on <Machine Learning, Just the Essentials!> (Insight, 2025).

This is a lecture covering LLM theory and practical examples based on <Large Language Models, Just the Essentials!> (Insight, 2025).

Wake up your sleeping coding brain. From Python basics to machine learning, all in one book! This book is an introductory computer science book based on MIT lectures, so even beginners who know nothing about programming can easily read it. Wake up your sleeping coding brain with topics essential for introductory programming, such as computational thinking and simple algorithms. Through brain-boosting problems provided in each chapter, we will look at practical topics such as data visualization, simulation, data calculation techniques, and machine learning.

The ultimate machine learning and deep learning tutorial that teaches you like a 1:1 private lesson with Hanbit Media's Honkong series, understand with hundreds of hand drawings, and practice right away with just a browser through Google Colab

This lecture guides readers to deep learning with the most appropriate pace and straight direction, one step from concept to formula and one step from coding. In addition, there are over 100 graphs, illustrations, and diagrams, so you can easily and quickly accept abstract concepts. Another unique feature of this lecture is that you can start practicing right away by simply accessing a web browser without installing a program. After comfortably understanding the theory, you can directly code and conquer four representative deep learning problems with your eyes, so it is not lacking as a textbook for deep learning. The concepts or terms that you must go over are reviewed twice in the ‘Wait! Let’s move on to the next’ corner in the middle of the text and the ‘Memory Card’ corner at the end of the chapter to increase the learning effect. Let’s quickly overcome deep learning head-on with ‘Do it! Introduction to Deep Learning.’

This lecture covers the contents of the Amazon bestseller <Hands-on Machine Learning 2nd Edition>. Using the representative machine learning library, scikit-learn, you will learn various machine learning algorithms and evaluation methods. Also, using the most famous deep learning libraries, TensorFlow and Keras, you will learn theories and practices from artificial neural networks to reinforcement learning. This lecture is not yet complete. One to two lectures will continue to be added every week.

RAG (Retrieval-Augmented Generation) from theory to the latest multimodal and agent-based RAG! This is a hands-on lecture designed to be understandable even for non-majors. From paper reviews to practical code implementation, it's designed so that even those encountering RAG for the first time can easily follow along.

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

We create an AI judge by combining CrewAI, facilitating LLM-based multi-agent technology, with knowledge graphs and inference systems.

This is a lecture covering LLM theory and practical examples based on <Large Language Models, Just the Essentials!> (Insight, 2025).

This course starts with the question "If I had been on the Titanic, could I have survived?" and completes a full-stack project that develops an AI model to predict survival probability based on actual data and serves it as a web service. You'll practice the entire process of AI and web development, from deep learning modeling using PyTorch, building a backend server with FastAPI, to implementing a user interface with Next.js.

From Multi Head Attention to the Original Transformer model, BERT, and Encoder-Decoder based MarianMT translation model, you'll learn Transformer inside and out by implementing them directly with code.

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 :)

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 directly through just 90 minutes of hands-on practice. We minimize complex theory and focus on code-centered practical flow, allowing you to directly experience "how AI makes decisions and uses tools." Beyond prompt engineering, let's take the first step into AI automation together.

Let's explore the types of applications or web services that can be built using LLMs.

Describes in detail the various language models developed from the inception of Natural Language Processing technology to the process leading up to the latest LLM models.

We will examine methods for collecting and analyzing user requirements to plan LLM services.

This is a course to master the core technologies of LLM basic theory, LangChain, and RAG. You can easily learn the latest AI technologies used in practice, from LLM basics!

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is the technology that has come closest to AGI at this point in time. In this lecture, we explore LLM and Agent technologies from a general public perspective.

Analyze over 40 types of economic indicators and stock data with AI to create your own powerful stock price analysis model that predicts not only S&P 500 and QQQ ETF, but also individual stocks!