[Deep Learning Expert Course DL1231] Backpropagation and Jacobian Matrix

This is a lecture that condenses the mathematics curriculum taught in middle and high school. You will learn the necessary mathematical knowledge and techniques through extensive practice for each item.

This course teaches you how to use LaTeX to write formulas.

This is a lecture on how to use NumPy and practice its application in real-world scenarios.

This is the fifth lecture in the [인공지능을 위한 파이썬] series. It is a course that provides practical exercises used in Machine Learning and Deep Learning, along with Python grammar.

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

[Python for AI] Third lecture in the series. Learn how to debug Python code.

This is the second lecture in the [Python for AI] series. It's a lecture that provides practical exercises used in machine learning and deep learning, alongside Python grammar.

This is a course that offers extensive practice in Python's basic syntax. This is a course that provides practical exercises used in Machine Learning and Deep Learning, along with Python's syntax.

This is a course where you learn the basic calculus and PyTorch's Autograd functionality needed to get started with deep learning.

This is a lecture on implementing practical code using PyTorch features based on the code directly implemented in Gradient-based Linear Regression (1). Additionally, it's a lecture where you'll theoretically learn about the necessity of data preprocessing, its theory, and its impact on learning, and implement it with practical code.

This is a lecture for perfectly understanding the process of training the simplest form of deep learning model using gradient descent.

This is a course that intensively covers gradient descent, the most fundamental learning algorithm in deep learning.

This is a lecture on data visualization using the Matplotlib library.

Learn virtual environment concepts and setup methods.

This is the first lecture in linear algebra, and it is a lecture that geometrically understands vectors and vector operations.

Limits of functions, derivatives, differentiation rules, derivative formulas, applications of differentiation

Covers essential linear algebra for Machine Learning/Deep Learning research.

Master the linear algebra content required for AI/deep learning by investing 10 minutes a day and completing it in one month.

Through this course, you will learn the basics of optimization theory for multivariable functions and gain a comprehensive understanding of vector functions.

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.