[PL 0301] Python Virtual Environment and Anaconda

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

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

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.

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

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

This is a lecture that provides a brief introduction to the field of computer vision.

We will summarize the theoretical concepts of MNIST, the most fundamental dataset in computer vision, and learn how to implement it using Torchvision's API as well as how to implement a custom dataset class directly.

This course provides a complete theoretical overview of the operations of convolutional and pooling layers, which are the core modules of Convolutional Neural Networks, and helps you develop implementation skills by building them yourself.

This lecture focuses on convolution, the most important operation in computer vision using deep learning.

Build a solid theoretical foundation of images essential before learning deep learning-based computer vision. Understand the essence of image data, such as pixels, resolution, and color representation, and cultivate practical programming skills to read, transform, and preprocess images using Python.

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 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.

Data analysis sounds a bit grandiose, but it is actually a process of establishing a hypothesis and verifying it based on data. You can learn how to analyze what data to verify your hypothesis using Python.

Are you new to coding or looking to take your coding skills to the next level? This course will introduce you to Python, a powerful and flexible language, designed for all levels of learners. Taught in a “follow-along” fashion, this course will allow you to learn by actually writing and running code. You’ll discover why Python is one of the most popular programming languages. From artificial intelligence to web development, learning the Python language offers endless possibilities, and it’s an opportunity to greatly expand your personal potential.

This course proceeds with Python theory and practical sessions that even non-majors can easily understand. This is the first step in the overall roadmap built for application development - the Python programming introduction course. [Complete-Roadmap] Python Basics->Advanced Python->Automated Trading-Backtest Development->Automated Trading-Real-time Development->Automated Trading-LLM Application