インフラン - ライフタイムキャリアプラットフォーム

LLM: A pro quickly delivers core content, from essentials to practical tips!

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.

Describes the need for LLM, its technical background, and basic concepts.

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!

Explains the basic principles of large language models like ChatGPT, focusing on theory.

Natural Language Processing (NLP) is one of the fastest growing areas of artificial intelligence. This course covers a wide range of topics, from the basics of NLP to the latest NLP techniques using deep learning. In particular, it provides an in-depth understanding of cutting-edge generative models such as ChatGPT.

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.

From the basics of deep learning natural language processing to the latest models such as Transformer and BERT, learn the principles and utilization methods of deep learning natural language processing (NLP) through various examples and practical code implementations.

Is this kind of introduction even important right now? Use the DeepSeek R1 model right away.

Learn the core techniques of 'Prompt Engineering' to get exactly the answers you want from AI. From a complete beginner's first steps to expert practical applications, maximize your AI utilization skills!

Python Korean Text Analysis and Natural Language Processing: Word Cloud Visualization, Morphological Analysis, Topic Modeling, Clustering, Similarity Analysis, Bag of Words and TF-IDF for Text Data Vectorization, Text Classification Using Machine Learning and Deep Learning, and How to Use Hugging Face

"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!

Learn the core techniques of 'Prompt Engineering' to get exactly the answers you want from AI. From a complete beginner's first steps to expert practical applications, maximize your AI utilization skills!

Now is truly an era where anyone can create their own service in just one day. While the previous Vibe Coding workshop focused on directly experiencing what Vibe Coding is all about and aimed for everyone to complete and deploy the front-end (visible pages) of the service they wanted to create, this workshop goes beyond simply creating a visible service. It places significant emphasis on a development approach that involves concretely imagining actual users and focusing on 'who will use this service and why.' Through the workshop, participants will naturally learn how to clearly define their target users and the problems to solve while creating their own services. It's perfectly fine if you're new to Vibe Coding! Even if you don't know coding or development, it's more than enough! Rather than simply following along, experience creating a truly usable service yourself in this workshop!

Now is an era where anyone can create services without coding. Selfish Club crew members implemented services for actual use with only the essential features needed within 3 weeks. These are cases where crew members who don't know how to code directly created services to solve problems using only natural language prompts. Vibe coding is no longer an unfamiliar concept! We're sharing real cases of 'what actual problems can be solved through coding' with everyone through actual examples!

This course is a systematic learning process for prompt engineering, which is a core technology for effectively utilizing large language models (LLMs) or generative artificial intelligence. It covers a wide range of topics from basic theory to practical techniques, as well as the latest application cases and security/ethical issues, providing practical help to LLM-based service developers, data scientists, and AI planners alike.

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.

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.

This is a course covering the GitHub notebooks and bonus content from <Build a Large Language Model from Scratch> (Gilbut, 2025). GitHub: https://github.com/rickiepark/llm-from-scratch/ <Build a Large Language Model from Scratch> is the Korean translation of the bestseller <Build a Large Language Model (from Scratch)> (Manning, 2024) by Sebastian Raschka. This book provides a way to learn and utilize the operating principles of large language models by building a complete model starting from scratch with OpenAI's GPT-2 model.

AI is not a technology that suddenly appeared. To properly see AI's present and future, you must understand 70 years of challenges and failures. 👉 "The moment you understand AI history, you can see both its principles and future together." Start right now!