[Deep Learning Expert Course DL1231] Backpropagation and Jacobian Matrix

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.

Limits of functions, derivatives, differentiation, derivative formula, applications of differentiation

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

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

This is a lecture that condenses the math content learned in middle and high school. You will learn the necessary mathematical knowledge and techniques for each item with a lot of practice.

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.

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 lecture on data visualization using the Matplotlib library.

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

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

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

Learn virtual environment concepts and setup methods.

The core of data science is expertly mimicking humans' ability to systematically analyze vast data! Through this process, you can refresh fundamental mathematical knowledge needed to understand scientific modeling, which forms the basis of data science.

In this course, you will learn how to solve various matrix calculation related problems using Python's SciPy library. Even if you do not know Python or have only a little prior knowledge, you will be able to solve the given problems.

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

This course is designed to help those who want to deeply organize deep learning and grasp the big picture by focusing on theory and context rather than hands-on practice. You can easily understand the mathematical and statistical background that forms the foundation of deep learning, and gain an intuitive interpretation from a developer's perspective of the principles behind major modern deep learning models such as AutoEncoder, GAN, Transformer, and AlphaGo.