Autonomous Driving with Python

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)

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

Implement Deep Learning Basics in Excel for Deeper Understanding!

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!

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.

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

We'll concisely summarize only the essentials of Python syntax!! For those who want to quickly learn only the must-knows like data types, conditionals, loops, and functions; for those who wish to understand Pythonic syntax in a short time, This is recommended for office workers, job seekers, and non-majors who want to master Python in a short period.

Let's begin developing with Copilot (copilot). With AI, even elementary school students can follow along!

A lecture consisting of special lectures by professors from Canadian national universities. It covers Python, Code, Scratch programming introduction/basics and practical applications. After completing the lecture, receive a global certificate from Codesprint : )

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

This is an introductory course in computer science and programming at MIT.

This course is designed to prepare for the Information Processing Engineer practical exam using only past exam questions. Instead of unnecessary theory or extensive content, it's designed to lead directly to 'passing' by focusing on past exam questions that are repeatedly tested in 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 who aim to pass efficiently in a short time.

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.

I created a course based on the content from the book "Becoming a Coding Test Passer - Python Edition". You can learn algorithms and data structures that frequently appear in coding test exams.

Want to take your code to the next level? 🚀 For beginner developers, I've prepared the 'Python Design Patterns' 📖 course. Complex design know-how is made easy to understand with practical examples. In this course, you'll learn these things: 💼 **Practical Example-centric**: Real-world examples you can apply immediately 📚 **Mastering Classic Patterns**: Master GoF's core patterns 🐍 **Pythonic Tips**: How to write more concise and efficient code It will boost your confidence in code design! ✨

This lecture is a coordination lecture. Coordination is the process of establishing a concept strategy that applies requirements and creating a prototype plan before starting design development. This lecture is suitable for web planners preparing for PL.

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.

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

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.

Trying to get a job, but it's not easy, is it? I, a liberal arts non-major and academy graduate, will share the 'effective portfolio' that I personally struggled for 'n' months to widely open the door to employment.

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.

This is a lecture for people who are starting development for the first time. It's about programming thinking, which should come before development languages or UI frameworks.

Just having that kind of developer portfolio makes it hard to get hired! Revealing the portfolio secrets that actually got people hired at major IT companies like Kakao, Samsung Electronics, and Naver!

Pass the Information Processing Engineer Practical Exam, first try! Code and theory, we'll teach you just the essentials. Complete mastery of past questions! Systematic lectures open the door to success.