Mastering Python Algorithm Fundamentals Faster Than Anyone

The most complete bible for passing all coding tests.

The core of software development ultimately lies in 'how quickly data can be found and processed.' Among numerous algorithms, the Hash Table is an essential tool that forms the foundation of modern programming languages and system architectures. This lecture goes beyond simple theoretical explanations to cover how students can 'immediately' resolve specific performance bottlenecks encountered in practice using hash tables.

This is an online lecture that systematically organizes pathfinding and spatial partitioning algorithms used in game production.

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

Learn Python from scratch by fully utilizing ChatGPT. Rather than tedious conceptual explanations, the focus is on students directly experiencing coding and developing problem-solving skills through example problems and practical exercises. After mastering Python's basic syntax, you can build your skills by solving various problems in Level 1 and Level 2. Furthermore, you can identify and supplement your weaknesses through an Exam structured like a real test. By regularly solving problems and utilizing ChatGPT, students can have a memorable learning experience without feeling bored.

This is a game development & algorithm course that focuses on the algorithms and principles of 2D game development from the ground up using Windows API and C++.

This is an introductory Python course based on interactive textbooks. Understand the principles of programming through a charming board game.

Learn the principles of Diablo 1-style PCG dungeon generation by implementing its structure in the console, with the theme 'Technology Supporting Game Development in the AI Era'. Expand this to 3D in Unity to complete a generation pipeline equipped with seed-based dungeon generation, mesh placement rules, and debug tooling. Through sample code and hands-on practice, connect to AI-assisted art production workflows, enabling you to create immediately applicable dungeon crawler prototypes.

You can understand the theoretical operating principles of the Kalman filter through easy examples.

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.

✅ Out of the entire series (1) ~ (7), (4) data structure, algorithm, object-oriented, programming language ✅ CS101 series lectures - From basic computer theory to the latest practical examples - Provides a wide range of basic knowledge

Even if you know nothing about coding, you can create your own scheduling app in just 4 weeks. Simply tell the AI, "Make an app like this," and paste the code you receive into Swift Playgrounds. Over 12 sessions—3 times a week, 30 minutes a day—you will complete a real app featuring adding/deleting/editing schedules, categories and notifications, filters and search, and even a calendar view. Don't worry if you run into errors; just ask the AI, and it will fix them for you. Start the challenge now to learn how to collaborate with AI, rather than just studying coding!

When learning Swift, the iOS programming language, it can be confusing when multiple concepts appear all at once. That's why I created this course with the principle that "when understanding a new concept, only previously learned concepts should be used." So this course is meticulously designed with a sequential structure between concepts, where only one new concept appears at each stage, and no other unfamiliar concepts appear at all. One concept per minute, 180 concepts total. From the basics of variables and constants to understanding memory structure from a CS perspective, and even the latest syntax for asynchronous processing. In 3 hours, you'll learn 180 core concepts in the designed order.

When learning Swift, the iOS programming language, it can be confusing when multiple concepts appear all at once. That's why I created this course with the principle that "when understanding a new concept, only previously learned concepts should be used." So this course is meticulously designed with a sequential structure between concepts, where only one new concept appears at each stage, and no other unfamiliar concepts appear at all. One concept per minute, 180 concepts total. From the basics of variables and constants to understanding memory structure from a CS perspective, and even the latest syntax for asynchronous processing. In 3 hours, you'll learn 180 core concepts in the designed order.