Top 10 Python Algorithms

Develop your ability to find the core of a problem and solve it with the 'Top 10 JavaScript Algorithms' that cover the most frequently asked types of problems!

AI does the coding, I get the results! Maximize practical programming skills, achievable even for beginners!

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

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.

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

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.

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.

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.

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.

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."