코드로 한 방에 이해되는 파이토치_프로젝트편

This course aims to systematically learn the essential core concepts and functions required for the Business Information Visualization Practical Exam (Tableau), based on the exam guidelines of the organizing institution. It is structured around hands-on practice to freely utilize commonly tested functions in real-world scenarios. Furthermore, you can improve your understanding of question types and problem-solving skills through mock exam A and B problem-solving.

"PyTorch in Code: The Fundamentals" is the perfect guide for anyone who wants to implement deep learning models using PyTorch, from deep learning beginners to developers looking for practical applications. Through 19 systematic lectures, you can perfectly master the core of deep learning, from PyTorch basics, deep learning algorithm implementation, overfitting problem solving, and CNN, RNN, and GAN models.

Get to the core of Python syntax with this quick guide!! Recommended for those who want to quickly learn the essentials, such as data types, conditional statements, loops, and functions; those who want to explore Pythonic syntax in a short amount of time; and those working professionals, job seekers, and non-majors who want to master Python in a short period.

The Management Information Visualization Competency certification course focuses on developing the ability to effectively analyze and visualize data to clearly convey information necessary for management decision-making. Through this, you can acquire various knowledge and skills that will help you develop your personal capabilities and improve your work skills as a talent with data-based strategic thinking and practical skills. This course was created for written test takers of the newly established 'Management Information Visualization' course in 2024. With content faithful to the question guidelines from the first to the third subject, we will help you pass the written exam.

"PyTorch in Code: The Fundamentals" is the perfect guide for anyone who wants to implement deep learning models using PyTorch, from deep learning beginners to developers looking for practical applications. Through 19 systematic lectures, you can perfectly master the core of deep learning, from PyTorch basics, deep learning algorithm implementation, overfitting problem solving, and CNN, RNN, and GAN models.

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.

You will learn how to process images and videos with OpenCV, and how to identify and recognize faces and various objects using deep learning. This is a course where you will learn fun projects such as drowsiness prevention, age and gender identification, counting the number of people passing by, face tracking, and YOLO that finds bad apples and tangerines, along with theories.

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!

AI Artificial Intelligence, it goes without saying that mobile is the trend. But how do you learn machine learning and deep learning and use them on mobile? Now, let's learn useful mobile Computer Vision projects that are used in daily life.

Special lecture to simultaneously prepare for Microsoft AI-900 qualification. Latest content reflecting May 2025 exam scope: AI overview, machine learning basics, computer vision, natural language processing, document intelligence, knowledge mining, generative AI, provided with theory and practice.

This course is a hands-on curriculum for instructors who are new to AI coding education. You will learn step by step from Python basics to simple AI models and prompt engineering.

If you invest 3 hours a day for 6 days, you can learn the basics of deep learning using PyTorch. Let's get started!

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

This is a lecture that studies Vision Transformer, one of the latest deep learning technologies, and implements a paper using Pytorch. Come experience the new future of the vision field with me!

Let's visually learn the principles of deep learning using Excel.

Learn how to use OpenCV and process images through a practical implementation of business card recognition. OpenCV is briefly explained, and the OpenCV functions required to implement business card recognition are explained in more detail. In other words, rather than a basic course, it provides an essence for the practical application of business card recognition. It also includes a method of utilizing Tesseract and Microsoft's OCR engine. The source code of this lecture is based on Python, but since you will learn the OpenCV classes and functions used and the image recognition and processing technology, it will not be difficult to apply it to Android and iOS based on this. Those who take the course will be provided with lecture slides and actual business card recognition source code. The lecture contents were further improved and verified through three offline seminars in January and February 2017, and the instructor has carefully recorded them again for online use. These are the OpenCV functions used in this lecture. cv2.Canny() cv2.GaussianBlur() cv2.VideoCapture() cv2.adaptiveThreshold() cv2.approxPolyDP() cv2.arcLength() cv2.boundingRect() cv2.boxPoints() cv2.contourArea() cv2.convertScaleAbs() cv2.createTrackbar() cv2.cvtColor() cv2.destroyAllWindows() cv2.dilate() cv2.drawContour s() cv2.findContours() cv2.getAffineTransform() cv2.getPerspectiveTransform() cv2.getTrackbarPos() cv2.imread() cv2.imshow() cv2.imwrite() cv2.minAreaRect() cv2.namedWindow() cv2.rectangle() cv2.resize() cv2.threshold() cv2.waitKey() cv2.warpAffine() cv2.warpPerspective()

By combining CrewAI, which enables LLM-based multi-agent technology, a knowledge graph, and an inference system, we will try creating an AI judge.

Explaining how to connect to a Python-based AI web server using FastAPI from a Spring Boot project.

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

AI Utilization Methods from a 5-Year Industry Veteran: From Tools to Insights! The popular special lecture from the Coding Bootcamp is now available to you.

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.

Let's have fun coding with various cute robots using artificial intelligence!

You don't need to know anything about coding. With just half a day of studying Vibe Coding, you'll be able to create programs without writing code. Let's learn the latest AI coding methods by directly creating 10 step-by-step examples, including web scraping, TTS, chatbots, and games.

This is the fifth lecture in the [Python for AI] series. This lecture covers Python grammar along with practical exercises used in machine learning and deep learning.