Deep Learning Paper Implementation with U-Net Implementation with TensorFlow 2.0 - Deep Learning Medical Image Analysis

Learn how to utilize various local LLMs (Qwen, Gemma) and explore different techniques to efficiently evaluate and improve the performance of LLM systems.

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.

Learn context engineering techniques for creating high-quality AI agents through hands-on practice.

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

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

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 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 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.

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

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.

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

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

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 lecture where you build your own MCP server using Cursor and experience vibe coding, which embodies the future of developers.

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

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

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.

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.

A course to learn deep learning paper implementation skills, implementing the GPT-1 (Improving Language Understanding by Generative Pre-Training) paper from scratch with TensorFlow 2.0.

Get a clear overview of exam types and trends at a glance! Sharpen your practical skills with a total of 120 questions. The perfect preparation for the AWS AI Practitioner exam, designed for systematic learning even when studying alone.

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.

This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.

Essential certification for the AI era, MS AI-900! 🚀 Did you know? ChatGPT operates exclusively on Microsoft Azure! If you want to properly utilize Microsoft Azure OpenAI, which exclusively provides the latest GPT models, now is the perfect opportunity. Grow into an Azure AI services expert through the AI-900 course and master enterprise-grade AI solutions that companies trust. Secure global official certification and enhance your competitiveness as a true AI professional!

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.

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 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 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 course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

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 :)

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!

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.

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.

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.