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

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

An introductory course that lets you experience the entire embedded development process at once—from installing Linux to controlling a robot When you want to get started with embedded systems, it can be overwhelming to know where to begin. This course is designed for those people to experience the entire process at once, starting with a single PC and ending with making a robot move. It covers everything from Linux installation and basic configuration, and preparing a Raspberry Pi, to reading circuit diagrams and datasheets, and practicing basic communication interfaces. Rather than focusing on depth, it focuses on understanding the overall structure.

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.

"From the basics to 7 domain projects, completing the A to Z of deep learning (41 lectures in total)." Go beyond simply calling models and learn how to build clean, efficient deep learning pipelines using PyTorch Lightning, the current industry trend. Grow into a confident AI engineer capable of handling any data by directly implementing 7 projects, ranging from stock price prediction to generative AI, medical imaging, and sound analysis.

This course will help you understand the value of IoT and how it changes our lives and society. You will learn the basics of IoT device programming, IoT device connection technology, big data analysis, and automation. You will understand digital transformation and its impact on businesses and society.

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.

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.

1. A practice-oriented lecture designed to help you pass the AICE Associate, Korea's only nationally recognized AI certification. 2. Learn to the level of performing data analysis and AI modeling based on Python.

You can learn about Text-to-SQL systems that generate and query SQL statements in real time in response to user questions.

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.

Build a small RAG system with three documents, then use actual execution logs to determine where to start fixing things—documents, retrieval, context, or generation—when the answers are wrong. Quantify the impact of improvements, including hybrid retrieval and reranking, multi-stage retrieval, and permission and operational checks.

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.

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 solve the challenge of knowing Python syntax but struggling to build services through hands-on experience at Naver and step-by-step practice.

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.

We provide guidance on what to study for those starting AI development.

Learn machine learning techniques using Python and improve your ability to extract information from real-world data and develop predictive models!

This course is a free program that guides you through setting up the AMD Vivado and Python development environments from scratch, as required for FPGA and NPU design projects.

- 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.

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.