(Using Raspberry Pi) Building an AI Artificial Intelligence Autonomous Driving Car

This is a course to learn in various languages how to call various open data provided by the public data portal from various programming languages and how to use JSON data provided as a result of the call.

Notion's powerful and convenient database features allow even beginners to easily build a DB. In this course, you will learn about the concept of table properties and practice, how to use the Notion database, and how to build a DB.

This is a series of video lectures for AWS beginners and college students enrolled in AWS Educate. We will briefly introduce the AWS registration process and free tier services.

This course is designed to prepare you for the Information Security Engineer written exam by concisely summarizing core theories and strengthening practical skills through problem-solving. Take a step closer to obtaining your Information Security Engineer certification with past exam analysis and practical preparation strategies.

Learn the key features of Photoshop CC 2020 to use Photoshop easily and quickly.

This is the Pen Tool series from the Photoshop Advanced Course provided by Photoshop Revolution Broadcasting.

Learn sophisticated features with Illustrator, which is constantly adding new features.

This is a lecture on the overall methodology of selling products on Coupang. In Part 1, you will learn step by step how to sell on Coupang with small capital and no inventory, starting from business registration.

This is a test preparation course to obtain the MOS Excel 2016 Core certification.

This is an Arduino lecture by Park Sung-jun on YouTube.

This course is designed to help office workers and practitioners utilize office programs more proficiently. Rather than simply learning the functions, the learning process focuses on real-world business scenarios such as document creation, data management, and presentation production. Through this course, students will systematically learn everything from the basics to the practical application of office programs like Word, Excel, and PowerPoint, naturally acquiring the skills to apply various functions to their specific tasks. This will help increase the efficiency of simple repetitive tasks and further strengthen practical competencies such as report writing and data analysis. In short, this course is a practical learning program that provides productivity-enhancing skills that can be applied immediately in the field, not only for beginners new to office programs but also for professionals who already have experience using them.

This is a free Unity basics course by Unity Hub root owner Dong-geun Joo.

This is an assembler course provided by Smoothie TV.

This is how to use Photoshop, taught in [Photoshop Revolution Broadcast].

Learn about word processor writing and practical skills by looking at the provided lecture materials. It consists of a summary of key points that you must know to pass the exam and solutions to past exam questions.

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

This is a course where you can study data structures and algorithms along with the object-oriented language C++.

I would like to introduce you to the Premier course by Nori Island on YouTube.

Run effective advertising with Google AdWords

Let's create a shopping mall with WordPress

An introductory course to experience the entire flow of embedded development, from Linux installation to robot control. When starting with embedded systems, it can be overwhelming to know where to begin. This course is designed for those people, structured so you can experience the entire process—from a single PC to moving a robot—all at once. We cover everything from Linux installation, basic configuration, and Raspberry Pi preparation to reading schematics and datasheets, as well as hands-on practice with basic communication interfaces. The focus is on understanding the overall structure rather than deep technical complexity.

In this hyper-connected era, you can easily understand IoT concepts, business characteristics, key technologies, and operating principles that are essential for employment and practice in the IT field. You can learn how to utilize IoT using Raspberry Pi.

You can understand the theoretical operation principle of the Kalman Filter through easy examples.

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.

Why this course is special: Key Advantages • Intuitive Visualization: Directly observe algorithm operations in real-time with Pygame 2D simulations • Practical Implementation Experience: Go beyond theory and internalize autonomous driving algorithms by coding directly • Master Core Algorithms: Focused learning of essential algorithms such as Dijkstra, Pure Pursuit, ICP, etc. • Step-by-step Advanced Learning: Systematic difficulty progression from basics to SLAM • Lidar-based SLAM: Practical map building and localization in unknown environments

We understand why Attention was needed and how it works by 'implementing it directly with code'. This lecture starts from the structural limitations of RNN and Seq2Seq models, experimentally verifies the information bottleneck problem and long-term dependency issues created by fixed context vectors, and naturally explains how Attention emerged to solve these limitations. Rather than simply introducing concepts, we directly confirm RNN's structural limitations and Seq2Seq's information bottleneck problems through experiments, and implement **Bahdanau Attention (additive attention)** and **Luong Attention (dot-product attention)** one by one to clearly understand their differences. Each attention mechanism forms Query–Key–Value relationships in what way, has what mathematical and intuitive differences in the weight calculation process, and why it inevitably led to later models naturally connects to their characteristics and evolutionary flow. We learn how Attention views sentences and words, and how each word receives importance weighting to integrate information in a form where formula → intuition → code → experiment are connected as one. This lecture is a process of building 'foundational strength' to properly understand Transformers, helping you deeply understand why the concept of Attention was revolutionary, and why all subsequent state-of-the-art NLP models (Transformer, BERT, GPT, etc.) adopt Attention as a core component. This lecture is optimized for learners who want to embody the flow from RNN → Seq2Seq → Attention not through concepts but through code and experiments.

Leverage Python to learn machine learning techniques, improving real data information extraction and prediction model development skills!

QML is a programming interpreter language for creating UIs provided by Qt. It is similar to JSON and can use JavaScript. Using QML and Python together, you can easily develop modern, advanced UIs.

Are you anxious every time you deploy an AI agent? Based on experience with major domestic corporations and global big tech companies, we will show you how to systematically measure and improve agent quality using LangSmith.

When you think of IoT (Internet of Things), the first thing that comes to mind is Raspberry Pi. We will cover everything from the basic installation process of Raspberry Pi to sensor control, building and utilizing various servers, and cloud connection.

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 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.

This introductory course for AI engineers is designed to provide a brief hands-on experience of the entire process, from data processing and model development to cloud, MLOps, and ethical considerations. It focuses on building practical skills by helping students understand the process of connecting models to actual services, rather than just creating them. The course includes hands-on exercises and examples so that even those learning AI for the first time can easily follow along.

This is a practical course on 'AI-based Circuit Design/Verification Automation' as required by professionals at Samsung Electronics and SK Hynix. Master the skills for LDO IP design and verification automation based on TSMC 180nm PDK, AI-based AXI-Lite RTL implementation, and Python/TCL/Batch script regression automation.

If you're planning to learn Python, pay attention to this course!!! It's completely free~ I highly recommend it for preliminary learning before taking formal courses or starting personal study.

- I am a Research Engineer/AI PM running a 4-year-old AI startup. - This video is a lecture for those who want to start studying artificial intelligence "now" or in "2026". - Looking at college students, I see many who have no idea how to get started with artificial intelligence. - I hope that after watching this lecture, you'll be able to start studying artificial intelligence. - For reference, this lecture is not about "using AI in my work" or "getting started with monetization methods using GPT". - This is a video about how students, graduate students, or developers can get started when they want to study artificial intelligence.

Students will learn the fundamentals of digital circuits, including Boolean logic, logic gates, circuit simplification, state machines, and Register Transfer Level (RTL) design. Additionally, students will learn design methods using Verilog.