Autonomous Driving with Python

This lecture examines the entire process of generating a photo mosaic from an algorithmic perspective. The input image is reduced to a set of representative colors matching the number of tiles through K-means clustering, then the Hungarian algorithm is applied to optimally match each color with a tile in a 1:1 correspondence. Through this, you will understand how image processing and combinatorial optimization algorithms combine to create a single visual output.

Let's visually learn the principles of deep learning using Excel.

Python + xlwings Work Automation Course Summary 1. Basic Section: Excel Control Fundamentals Cell Handling / Format Application / Image & Chart Insertion 2. Application Section: Automation Scenarios Automatic Email Sending / Report Summary Automation (LLM) / Image Classification Tasks (Teachable Machine)

Deep learning is a technology that learns data through neural networks composed of combinations of complex functions. In this lecture, we will mathematically understand the core concepts of deep learning and analyze them from the perspective of matrix operations. In particular, utilizing Python's NumPy library, we will visually examine how parameters are updated by directly implementing the forward and backward propagation processes of deep learning. Even the seemingly complex neural network structure becomes clear when analyzed with matrix operations. This lecture focuses more on understanding concepts than coding and is suitable for students who wish to intuitively grasp the principles of deep learning mathematically.

Implementing deep learning basics with Excel to deeply understand their meaning! Learn how image data is processed!

Implement Deep Learning Basics in Excel for Deeper Understanding!

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

Deeply understand deep learning basics by implementing in Excel!

Pre-Trained Model (VGG16) / CAM / Grad-CAM / YOLO / STYLE TRANSFER / PIX2PIX / CYCLEGAN Implementing Stable Diffusion Directly

Hot image generation model! Let's understand it by implementing it from the basics! Let's implement AUTO ENCODER / VAE / GAN / DIFFUSION together!

Deep learning is a technology that learns data through neural networks composed of combinations of complex functions. In this lecture, we will mathematically understand the core concepts of deep learning and analyze them from the perspective of matrix operations. In particular, utilizing Python's NumPy library, we will visually examine how parameters are updated by directly implementing the forward and backward propagation processes of deep learning. Even the seemingly complex neural network structure becomes clear when analyzed with matrix operations. This lecture focuses more on understanding concepts than coding and is suitable for students who wish to intuitively grasp the principles of deep learning mathematically.

This course is designed for those with absolutely no programming experience, helping you learn Python, a popular programming language, from scratch. Python is one of the most suitable languages for beginners to learn due to its simple syntax and readability. It is used in various fields such as web development, data analysis, artificial intelligence, machine learning, and task automation, and is a very promising technology for the future. In this course, you can systematically learn Python's basic concepts through practice, including variables, data types, conditional statements, loops, functions, lists, dictionaries, and more. Furthermore, you can also learn practical skills by directly building actual applications or tools. We provide friendly and easy-to-understand explanations, along with abundant examples and practice problems, so that even those with fear of programming can easily follow along. By writing and testing code yourself, you can naturally increase your understanding and continue learning without getting tired. Upon completing this course, you will be able to write simple programs yourself and gain confidence in solving various problems using Python. Now is the perfect time to start Python as your first step towards career advancement or change.

It's okay if you don't know Python! This course is a hands-on lecture designed so that even those who are new to programming can follow along. By creating the classic game Space Invaders yourself, you can naturally learn Python basic syntax and 2D game-programming using Pygame. Since you learn while making a game, there's no time to be bored, and you can feel the sense of accomplishment of completing something with your own hands.

Based on learning fundamental concepts of programming and Python, we solve mathematical problems using Python. (Solutions and roots of equations, drawing graphs)

Understanding programming concepts is now a must, not an option! Are you confused about suddenly collaborating with developers? Do you want to start a career as a developer? Programming, which felt difficult to non-majors! Let's go together from concepts to basic practice.

This course prepares you for the Information Processing Engineer practical exam solely through solving past exam questions. Instead of unnecessary theory or extensive content, it's designed to lead directly to 'passing' by focusing on past questions that repeatedly appear on actual exams. It reflects the latest exam trends, allowing you to quickly grasp key points and question patterns, and you can learn answer-writing techniques through the problem-solving process. This is a course optimized for test-takers aiming to pass efficiently in a short time.

Based on over 100 deep learning training sessions, this course systematically organizes the core foundational theories that students found most challenging. The course connects mathematical intuition, model learning principles, and code implementation step by step in a way that even non-majors can understand, deeply covering the fundamental structure and operating principles of how AI models learn, rather than just library usage. This is a practical introductory course designed to help you grow into a skilled engineer who understands AI principles by implementing core deep learning foundational technologies such as gradient descent, loss functions, optimization, perceptrons, multilayer neural networks, and backpropagation through both formulas and code.

■ We carefully select only the core Python grammar and organize it for you in a short time. ■ Quickly learn only the essential content such as data types, conditional statements, loops, and functions. ■ Perfect for those who want to quickly grasp a "Pythonic" grammar sense in a short period. ■ Structured so that working professionals, job seekers, and non-majors can follow along without burden. ■ A practical Python introductory course where you learn quickly and apply immediately.

News crawling in the automation lecture... Are you bored? This is a lecture on developing an automation program that will make you an Instagram influencer.

These days, it's become so famous that you can't afford not to know about it - Nano Banana? Gemini 3.0? In this course, you can learn to handle everything with the Gemini API.

Pass the Information Processing Engineer Practical Exam in one shot! We'll pinpoint exactly the essential code and theory you need. Master past exam questions perfectly! Open the door to success with systematic lectures.

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

“There are many ways to solve problems, but why do we feel lost during interviews?” This lecture offers coding interview survival strategies directly from a current engineer working in Silicon Valley. Focusing on problems frequently encountered in real interviews, we'll guide you not just through simple solutions, but also how to articulate your thought process and how you'll be perceived from the interviewer's perspective. Designed for mid-level developers and above, the curriculum is optimized for real-world interviews. If you are preparing for coding tests and technical interviews, gain practical skills and confidence through this lecture.

This course covers everything from core recommendation system algorithms to practical implementation. - Content-based filtering - Collaborative filtering and deep learning-based recommendation model implementation - Two-step recommender systems implementation - Hands-on practice using PyTorch/RecBole - Industry know-how and recommendation result visualization

ChatGPT-aided Python for Programming Dropouts.

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

You can build the basic stamina to leave work on time by automating report writing.

Create a program using the GPT API and even generate images and videos!

What if ChatGPT were embedded within your own application? In this lecture, we will develop various programs using the ChatGPT API. We will cover all processes in detail, from basic API usage to program creation and deployment.

Regardless of the field! This is a lecture that covers the core fundamentals that developers must know in practice in an easy + code-oriented way. I highly recommend this lecture to all junior developers and job seekers who want to grow.