CUDA is a GPU programming language. It is a language that was created by modifying C and C++, so anyone who is a C or C++ developer can easily approach it. However, you must understand the characteristics of GPU machines and CUDA programming. This course understands GPU machines and writes CUDA to execute matrix multiplication, which is the basic operation of deep learning. Through this course, you will be able to handle GPU programming skillfully.

Flask is widely used as a Python backend framework. Flask_SQLAlchemy is a database orm used in Flask. If you know Flask_SQLAlchemy, you can do web development with concise code.

React is the most popular modern front-end framework, but it is difficult to learn. To solve this problem, Hook was introduced. This lecture will help you learn React easily using Hook.

There are so many benefits to developing Spring Boot with Kotlin. I think it would be good to learn about them through this lecture.

This is a SQL lecture designed to help marketers learn SQL easily. We introduce the essential SQL grammar and analyze real data obtained from Kaggle.

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

Tensorflow uses a complex graph structure even for simple arithmetic. Why? Because Tensorflow needs derivatives when performing Gradient Descent. Graph structure is a means to easily derive those derivatives. I've mathematically explained why.

This is a friendly explanation of things that beginners of Spring Boot and Kotlin will encounter while developing web from a beginner's perspective.

In this lecture, you will learn the basic theory of reinforcement learning.

This course will help you build a solid foundation in the basic concepts of reinforcement learning through essential theoretical learning on deep reinforcement learning, and learn the principles of deep reinforcement learning in detail through hands-on practice of implementing actual code using TensorFlow.

Make your own AI autonomous vehicle! This is a course where you can learn about AI and reinforcement learning in the most fun and fastest way. This course was produced by the developer ranked #1 in DeepRacer Korea.

Through this course, you will learn and use various functions of machine learning agents, such as multi-agents, curriculum learning, and distributed learning. You will also learn about reinforcement learning algorithms that can respond to curiosity-based exploration and variable inputs.

Through this course, students will learn various reinforcement learning theories and implement them themselves, as well as create a reinforcement learning environment to test the reinforcement learning algorithm implemented using Unity Machine Learning Agents.

Let's implement a deep learning framework directly with Excel! Let's solve supervised learning/unsupervised learning/reinforcement learning with the implemented framework!