開発者の未来！AIを利用したバイブコーディング Part 1 - Cursorを利用した自分だけのMCPサーバーの作り方

AI Agent: A total integration of the latest AI technology! Implement various AI agents and learn to build your own AI agent using LangGraph.

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.

Data Science(DS) & Artificial Intelligence(AI) job role analysis, plus a learning & employment roadmap for building a career in these roles.

This course teaches you how to implement deep learning papers by implementing the YOLO (You Only Look Once) paper from scratch using TensorFlow 2.0.

This is a course to learn the concepts and usage methods of the LangChain library, and to build your own ChatGPT using the LangChain library.

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.

I'm learning how to build practical AI agents using LangGraph while working on various AI agent implementation projects using LangGraph.

This lecture covers building practical RAG (Retrieval-Augmented Generation) systems while developing an AI customer service chatbot (AICC).

This course covers data analysis using ChatGPT and Pandas utilization. Get a preview of the changed future ChatGPT will create.

This course covers work automation using MCP (Model Context Protocol) via diverse case studies: Playwright crawling, Figma design, Notion data organization, Excel data analysis.

This is an all-in-one lecture where you can learn the entire process from the basics of deep learning/TensorFlow/computer vision to practical applications through a practical vehicle license plate recognition project. Through various practical exercises, you can develop practical skills to apply the latest deep learning models to custom datasets.

This course will help you build a solid foundation in the basic concepts of reinforcement learning through essential theoretical learning on deep reinforcement learning, and learn the principles of deep reinforcement learning in detail through hands-on practice of implementing actual code using TensorFlow.

Learn the basics of machine learning step by step through various Kaggle examples, and learn vivid project experiences and practical tips from hard-to-access field machine learning engineers all at once.

This course teaches you how to perform Object Detection using the latest high-performance deep learning model using the TensorFlow Object Detection API through simple Colab exercises, and learns the principles of the latest deep learning Object Detection models.

Through AI company reviews, we examine how AI technology will change our present and future through various cases. By examining the necessary elements for carrying out AI projects, we prepare for the upcoming AI Transformation.

This course will teach you the core theories of deep learning and how to implement deep learning code using the latest TensorFlow 2.0.

This is a lecture where you can learn how to use Git & GitHub step by step from the basics to advanced content through various practical exercises. Understand the principles through intuitive examples and learn how to use Git & GitHub through practice.

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 is a lecture on creating an automatic stock trading bot using the IBK Investment & Securities Xing API. Create your own automatic stock trading bot that trades automatically 365 days a year.

A course to learn website crawling methods using ChatGPT. Get a preview of the transformed future ChatGPT will create.

AI Agent: A total integration of the latest AI technology! Implement various AI agents and learn to build your own AI agent using LangGraph.

What should be done when building a generative AI or LLM-based RAG (Retrieval-Augmented Generation) system, but the desired performance isn't achieved and there's no suitable solution? This lecture presents methods to improve RAG performance based on Cognitive Load theory. Through this lecture, you will understand the limitations of LLM context windows and learn how to effectively manage cognitive load in RAG systems. It is a practical-level theoretical lecture covering Chunk size and structure design, high-quality Chunk generation techniques, dynamic optimization, performance evaluation, and practical techniques.

This is a hands-on tutorial on how to integrate with IntelliJ and develop quickly using the Model Context Protocol (MCP).

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

This course teaches you the know-how of the entire process, starting from a single idea, through planning, design, development, and up to app release on each platform. Start developing your own monetization app right now.

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.

The Latest Trend in Generative AI, AI Agents! How to Stay Ahead with Cursor, which handles all development tasks, and MCP, which expands functionality!

Develop various features using Spring WebFlux. This course will teach you WebFlux's essential concepts and practical implementation.

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.

AI writes on my Slack and Notion for me? Work smarter with MCP that surpasses the limitations of existing AI!

We invite you to the fascinating world of coding where you analyze and visualize financial statements using only prompts. Without a single line of code, you create and deploy a service in consultation with AI.

Learn the easiest way to handle AI with Spring AI. Let's learn Spring AI by building a RAG chatbot.

Perfectly master the working process of MCP by creating a TodoList with Claude and IntelliJ.

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