[PyTorch] NLP を簡単に素早く学ぶ

This is a lecture for non-majors or high school students who are studying data science and artificial intelligence for the first time. We will teach you the concepts of artificial intelligence and machine learning and what efforts you need to make to get a related job.

This is a basic R programming course for beginners. Just follow along with me and type in the code twice. You will naturally become a beginner!

Theory and practice are different. We will understand the basic concepts of machine learning and introduce the core concepts and theories of various models that you must know. And we will share various techniques and know-how that are helpful in practice while handling various data.

This is a PDF textbook that covers the basic grammar of R, frequently used functions, and useful intermediate to advanced techniques.

This course is for those who want to study machine learning but lack statistical knowledge.

Learn how to collect and manage all stocks listed on the stock market. Create a dashboard using shiny that automatically collects new stock prices every day and can also identify stock trends by industry.

Learn about GAN (Generative Adversarial Networks) in an easy and accurate way.

Learn MLP, CNN, and RNN, the basic framework of deep learning, quickly and easily.

We will learn about Q-learning and Deep Q-learning, and have time to implement reinforcement learning in R. We will cover the entire reinforcement learning content, from Deep Q-network to Self-imitation learning and Random Network Distillation.

This is a lecture that teaches you how to do data science through R. You will learn line by line, from loading data to building models and model performance strategies.

We cover frequently used functions for R programming, how to write code efficiently, and intermediate techniques.

Learn how to draw networks using R. Rather than drawing the network itself, we focus on building the shape of the data that will go into the network and how to draw the network nicely.

You have learned the basics of programming, crawling, and text mining, but have you ever felt lost when it comes to actually doing a project? This is a lecture where you will go through a project with me from start to finish.

This is a practical text mining project. This is a lecture that covers everything from data collection to sentiment analysis and data visualization all at once!!

This course covers the basics of R programming for those who have no knowledge of R programming.

A course for DS/AI beginners aiming to study. It will explain DS/AI concepts and fields, guiding what and how to study.

This is a lecture that will teach you step-by-step how to use R to analyze top keywords, wordclouds, sentiment analysis, and topic modeling in text.

I will make it so that you can crawl freely. Most of the lectures are conducted through live coding. Please follow along line by line while I code!

Build basic R programming skills and learn basic crawling techniques. We will explain each line while writing the code yourself.

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.

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.

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.

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

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!

If you have a vague curiosity about generative AI but don't know where to start, this course is the perfect answer. It's structured to explain the core principles of AI technology clearly and concisely, allowing anyone to quickly grasp the essence. We'll go beyond simply knowing 'what it is' to deeply explore why it works that way and how to utilize it effectively. This course will serve as the most reliable starting point to help you confidently and proactively leverage technology in the rapidly changing AI era.

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students can directly use the GPT API to create chatbots and run them on the web using Streamlit. By experiencing everything from basics to hands-on practice, you can solidly establish your first steps in AI utilization.