Shin Kyung-shik's Deep Learning Odyssey - Image Basics for Computer Vision

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 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.

Learn virtual environment concepts and setup methods.

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 data visualization using the Matplotlib library.

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 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

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

You can acquire an overview of machine learning and deep learning, how to use basic tools, and the Python language knowledge necessary for learning in a short period of time.

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

Numpy and Pandas are the foundation of data analysis and the backbone of AI frameworks, and I've packed this course with the core practical know-how I've accumulated through my work and teaching experience. I've designed it with a hands-on approach so that marketers, planners, and beginner analysts can easily follow along with data cleaning and processing—the areas where they most often get stuck. Through this course, anyone can develop the skills to handle data and produce analysis results on their own, without needing help from developers.

This is a basic DFS lecture that explains the way a liberal arts major and working developer studied to get a job :) We have prepared a lecture where you can learn by solving problems yourself, with minimal boring theory lectures! After taking this lecture, you will be able to solve silver level DFS problems on your own according to Baekjoon standards.

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!

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.

Learn about Python NumPy, a representative library in the field of data science. Learn NumPy and lay the foundation for learning data science!

Develop a deep understanding and intuition for calculus. Solve problems and implement algorithms by hand and in Python.

This is an introductory course designed by SuCoding, an IT author and coding creator, using a stable learning method that ensures even non-majors can complete it without frustration. We have boldly removed complex theories and selected only the essential syntax to deliver a practice-oriented experience. Even if you are starting to code for the first time, by following the lecture to the end, you will be able to create your own programs using Python.

Theory, numerical approximations, intuition, and visualization in integral calculus. Enhance your math-coding skills.

Let's acquire the Python coding skills necessary for artificial intelligence by learning the basics of the Python programming language!

If you have repeatedly failed at studying Python, this is a must-take course. We will teach you how to study practical Python that you can actually use, in connection with "blog automation."

Django Base Camp is a course where beginners to intermediate learners can systematically master the core of the Django web framework. Through project-oriented practical lessons, you can quickly develop the ability to build web applications.