Kalman Filter 1: A lecture explaining the exact mathematical principles of the Kalman filter through examples

Master everything from implementing kinematics and dynamics theory with code written entirely from scratch, without open source, to communication interfaces for multi-joint manipulators.

LeetCode explained by Dalle, a Korean developer working at a Silicon Valley startup! This is a practical LeetCode problem-solving course that doesn't just provide answer codes, but compares multiple approaches for each problem, analyzes time and space complexity together, and explains why certain solutions are more efficient. First, we write the most intuitive approach and analyze its time/space complexity to identify bottlenecks and improvement points. Then, we explore alternative approaches applying various data structures and algorithmic techniques, comparing the performance differences and pros and cons of each method. Through this, it helps you develop problem-solving skills to design efficient solution strategies on your own when facing new problems.

This course provides an interactive introduction to discrete mathematics for computer science and engineering. Learners will study fundamental mathematical concepts, discrete structures, and discrete probability theory, developing the ability to apply these to various fields of computer science.

The most complete bible for passing all coding tests.

This course covers essential mathematical concepts widely used in mechanical engineering, focusing on linear algebra and ordinary differential equations (ODEs). Learners will explore numerical methods for solving systems of equations and apply these techniques to real-world engineering problems.

This lecture introduces and solves the Qualification Round 1st problem of Google Codejam, a Google algorithm competition.

I hope that through this course you will gain a clearer understanding of grammar and gain confidence in solving problems.

When you learn machine learning, you learn how to improve accuracy. But the moment you try to put that model onto an actual chip, questions no one has answered remain. How many multiplications does this model perform? How much does it need to remember, and for how long? Can the chip specifications you are targeting handle those requirements? Machine learning textbooks talk about accuracy, and semiconductor textbooks talk about circuits, but materials that bridge the gap between them are rare. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs. The model with the highest accuracy often turned out not to fit on the board. In this course, you will build six algorithms directly in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy each time. Then you will convert those figures into the resources of an actual FPGA board. Saying that something requires a certain number of multiplications is not enough to gauge its size, but knowing that it uses a certain percentage of the board makes the decision clearer. After nine Labs, when the six models are brought together in a single table, an unexpected picture emerges. We spent nine lessons counting multiplications, but multiplications were not what held us back. After that, we reduce floating-point values to integers, manage bit widths, and even establish criteria for evaluating the circuit. You do not need an FPGA board. The course is conducted entirely in Python, and the board is used only as a reference for converting the measurements.

RUR-PLE is an educational software created to help students learn software development more easily using Python. Here, you will learn what can be done with software and develop computational thinking skills through simple hands-on exercises.

The Code Puzzle Clear Challenge is a 4-week intensive training course based on "Learn to Code 1" in Swift Playgrounds, designed to develop "computational thinking" by giving commands to make a computer move as intended. By solving puzzle missions in each session, participants naturally learn to break down problems into smaller steps (decomposition), recognize patterns to organize repetitive rules using for-loops or functions, and make logical judgments to change actions based on specific situations. Beyond simply finding the right answer, participants reach a level where they can explain and refine "why they solved it this way."

This course makes it easy to learn the essential mathematical concepts used in games. People who are already proficient in math often take this knowledge for granted, making it difficult for them to teach from a beginner's perspective. This course assumes you have no prior knowledge and explains everything from the viewpoint of a complete beginner or non-major.

This is a hands-on course where you'll build an AI-based autonomous driving car using Raspberry Pi and various sensors.

This course is for those preparing for the CosPro Level 1 exam, covering the COS Pro Level 1 sample problems released by the exam organizer, YBM Net Co., Ltd.

This course is for those who want to understand the calculus and backpropagation formulas behind deep learning. Starting with functions and limits, you will learn derivatives, the chain rule, gradients, and gradient descent in sequence, then manually calculate the forward propagation, loss calculation, backpropagation, and weight updates of a small neural network. Explore the concepts through animations without coding or installation, and connect the formulas to the AI learning process.

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.