Linear Algebra Leading to Machine Learning/Deep Learning

This is a lecture that studies the physical information neural network, one of the next-generation innovative technologies of deep learning, and implements it directly using Pytorch. Let's learn the next-generation innovative technology of artificial intelligence with me!

This course provides essential knowledge needed to understand the structure and operating principles of various artificial neural networks and create good models.

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!

This is a lecture on implementing various artificial neural networks using PyTorch, a deep learning framework with very high utility in the field of artificial intelligence.

Learn how to understand advanced machine learning models without math and implement them easily in Python.

This course will teach you from the early YOLO model, a real-time object recognition model, to the latest model. In addition, you will learn various deep learning techniques along with object recognition.

This is the optimization theory required for AI/deep learning, computer vision, computer graphics, etc. Optimization Theory 1 mainly deals with the definition of multivariable functions and differentiation of multivariable functions. Why is that? Because all optimization problems are expressed in the form of multivariable functions. If you acquire the exact definition of multivariable functions and the concept of differentiation, the theoretical approach in the above fields will become considerably easier.

This course is ideal for those who want to focus on theory and context rather than practice, aiming to organize their knowledge of deep learning in depth and grasp the big picture. It makes the mathematical and statistical foundations of deep learning easy to understand and provides intuitive interpretations of key modern models—such as AutoEncoder, GAN, Transformer, and AlphaGo—from a developer's perspective.

This course is a course that practices the basics of machine learning mathematics and statistics using Python. Machine learning and deep learning require programming skills, but they also require a mathematical background to understand the principles. Through this course, you can gradually learn the basic mathematics required for artificial intelligence.

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

This is a lecture where you can easily learn important mathematical concepts used in games. People who are well-versed in mathematics take this knowledge for granted, so they cannot teach it assuming you know nothing. I explain from a beginner's perspective, truly assuming you are a non-major.