Generative AI for Designers🧙‍♂️ We reveal practical know-how to maximize design work efficiency with generative AI like Canva and Midjourney.

How to Build an Efficient Startup Workflow System with Just Notion 🧑🏻‍💻 (Practical Workflow Templates Provided by Practitioners)

You can easily create your own blog with just GitHub Fork! This is a service that allows you to manage a learning blog (TIL) and GitHub lawn together.

Would you like to experience web server development? We will help both non-majors and front-end developers take their first step into server development. Let's efficiently explore servers and databases using familiar JavaScript syntax all at once, and understand servers!

No more toy projects! We're revealing all the secrets to building a secure authentication system with JWT and Redis, actually used by developers in the field.

A basic textbook and practical guide covering principles and practical uses of various generative AIs like ChatGPT and Claude, and for their effective introduction and utilization.

Can you answer the question, "Why do we need classes? 🤔" This covers everything Python developers should know about Python classes!

New to both Blender 3D and 3D in general? A customized 3D introductory lecture for complete beginners, taught directly by a KakaoTalk 3D emoticon artist.

Say goodbye to complicated collaboration tools! 👋 From Markdown to databases, the essential skills of a workaholic can be done with Notion 🚀

This course provides a systematic learning experience, covering everything from basic JavaScript synchronous/asynchronous concepts to modern asynchronous processing methods like Promise and async/await, as well as practical HTTP communication.

Learn the fundamentals of the web, networks, and HTTP that every developer should know, in a code-based, real-world, data-driven way.

If you want to create a web page, try learning HTML/CSS. With HTML, which defines the structure of a web page, and CSS, which defines the style of a web page, you can implement various content and designs.

Essential in web development! Learn JavaScript, a popular language widely used in various fields, through basic concepts and practical examples!

Django Base Camp is a course where beginners to intermediate learners can systematically master the core of the Django web framework. Through project-oriented practical lessons, you can quickly develop the ability to build web applications.

Python Basecamp is a course for beginners who want to master the basic concepts of Python. It improves your skills through fundamental concepts and practical examples.

Python made easy! Learn Python like a game with various stories in 'Winib World'!

This is a tutorial lecture where you can learn simple usage of PyScript.

Know it! Well! Just! Neatly! Sensefully! This is a free lecture from the book Flex & Grid. You can use the open source link, the book, and the Notion link for free. This is a non-profit project, so using the textbook is possible without permission.

Know! Well! Just! Neatly! Sensefully! This is a free lecture on the GitHub core concept book. You can download the book and Notion link for free. It is a non-profit project, so using the textbook is allowed without permission.

We believe that you need to know HTML and CSS well to become a solid front-end developer.✍ From the types of HTML and CSS to practical applications, this course will teach you how to use the language correctly. We prepared it with great care, so please enjoy it :)

In the second season of Professor Kim Seong-hun's "Deep Learning for All" course from Hong Kong University of Science and Technology, we will learn about "reinforcement learning."

To keep up with recent deep learning trends, we examine the context of deep learning's groundbreaking advancements.

Understand the basic concepts and latest trends of big data, and through technical understanding of big data analysis techniques, extract data patterns to present methods for business application. Through big data, you can create new businesses and develop insights into future business environments.

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

If you want to be recognized as a machine learning expert in large-scale data environments—from understanding the core framework of Spark Machine Learning to SQL-based data processing through high-difficulty practical problems, and the ability to implement optimized machine learning models through business domain analysis—join this course.

Through this course, students will learn various reinforcement learning theories and implement them themselves, as well as create a reinforcement learning environment to test the reinforcement learning algorithm implemented using Unity Machine Learning Agents.

This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

This is a Python basics course for starters who want to grow as data analysts. - Those who want to do data analysis but don't know Python - Those who want to learn machine learning with Python - Those preparing for the Big Data Analytics Engineer practical exam - Those who want to develop machine learning-based applications with Python

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.

Let's implement a deep learning framework directly with Excel! Let's solve supervised learning/unsupervised learning/reinforcement learning with the implemented framework!

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

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.

This introductory course for AI engineers is designed to provide a brief hands-on experience of the entire process, from data processing and model development to cloud, MLOps, and ethical considerations. It focuses on building practical skills by helping students understand the process of connecting models to actual services, rather than just creating them. The course includes hands-on exercises and examples so that even those learning AI for the first time can easily follow along.

This course is an introductory program designed so that learners who are new to AI technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and conduct hands-on practice directly generating images using various AI tools. Upon completing this course, you will become much closer to AI and acquire the capability to create AI images that can visually implement creative ideas.

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students will build their own chatbot using the GPT API and deploy it on the web using Streamlit. By experiencing everything from the basics to hands-on practice, you can build a solid foundation for your first steps in utilizing AI.

See exam types and trends at a glance! Strengthen your practical skills with approximately 100 questions. The perfect preparation for the AWS AI Practitioner exam, allowing for systematic study even when preparing on your own.

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 :)

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.