Linear Algebra for AI

If you're a React developer, you've probably wrestled with useMemo and useCallback at least once, wondering "Do I really have to do this manually?" Finally, the React Compiler has arrived to solve that dilemma. However, whenever new technology emerges, reading through extensive official documentation from start to finish can be overwhelming. That's why I've carefully analyzed the React Compiler official documentation and prepared a course that extracts only the essentials for easy understanding. This course covers: - Why: Why the React Compiler was created and how it differs from existing approaches - How: Installation and configuration methods to apply directly to your projects - Troubleshooting: Key precautions emphasized in the official documentation and error resolution tips Now break free from the pain of manual memoization and focus solely on business logic. This course will upgrade your React development experience to the next level.

🌟 From the basics of JavaScript to advanced levels, start with the Real! JavaScript course! 🌟

This lecture is for those who have heard of the term deep learning but don't know where to start :)

When researching AI or conducting a project using it, basic paper implementation is essential. Let's upgrade our practical skills by implementing an actual paper through this lecture!

"The deep learning code runs, but why do the formulas in papers look so unfamiliar?" As you study AI, you eventually hit a wall of mathematical formulas. If you trace the source of that wall, it usually leads back to not having a solid foundation in the most basic mathematical language. Starting with the language of sets, this course covers core concepts across 18 lectures, including equations, functions, sequences, exponents/logarithms, and trigonometric functions. We don't make you memorize formulas. While we briefly touch upon how these connect to the context of AI, the primary goal of this course is one thing — to build an unshakable foundation for when you later learn linear algebra, calculus, and probability/statistics. As the first part of the Suminjeongeum series, this course will provide you with the common language needed to properly digest the remaining three parts.

In the era of big data, data science is gaining attention as a solution for scientific business decision-making. Through this course, you will learn about various data analysis methods and utilization methods and be able to apply them to your work.

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

Through a systematic curriculum of 40 lectures, you will learn everything from the basic theories of AI to the applied AI technologies of each algorithm. Through mathematical intuition and R coding algorithm learning and practice, you will develop immediately applicable AI model development capabilities, covering everything from designing algorithm models for data analysis, machine learning, and their application tools to selecting and utilizing them.

This course provides a solid foundation in the essential linear algebra required for AI learning and research. It covers core concepts frequently encountered when preparing for AI graduate school admissions, reading research papers, and conducting research, as well as fundamental numerical linear algebra techniques.

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.

Master the linear algebra content required for AI/deep learning by investing 10 minutes a day and completing it in one month.

With the advancement of generative AI, many companies are pursuing AX (AI Transformation). However, in the actual field, there are not many educational programs that systematically teach what to do and how to do it compared to the demand that "AX must be done." While most AI education focuses on how to use generative AI or write prompts, this course focuses on the "AX Strategy Planning Methodology" for transforming corporate tasks and services into AI-based ones. This course is based on the practical experience of a 25-year expert who has performed DX/AX consulting and training for large corporations, the financial sector, public institutions, and various companies in the actual IT field. It allows you to learn from a practical perspective not just how to use AI well, but which tasks should undergo AX, to what extent AI should make decisions, how to control risks, and how to design and verify AX projects. Furthermore, it systematically covers core content necessary for actual AX implementation, from understanding the essence of AX to AI thinking systems, AX design methodology, Human-AI role separation, Guardrail design, and KPI and verification structures. In particular, it goes beyond simple theoretical education. Based on real corporate cases such as insurance underwriting, customer consultation, manufacturing quality, internal work support, marketing recommendations, development productivity, and management decision-making, it provides guide templates and sample materials for AX problem definitions, AX hypothesis definitions, AX performance designs, AX service structure diagrams, Guardrail designs, and AX verification plans. This ensures that after taking the course, you can secure the practical capabilities to easily plan and promote actual AX tasks. The biggest differentiator of this lecture is that it teaches how to plan and design AX from the perspective of someone who actually performs AX projects. Specifically, by providing AX strategy planning output templates and seven industry-specific AX cases that can be used in the field, it is designed so that students can refer to cases most similar to their own work and apply them immediately by modifying them to fit their environment. In short, this is not a course to create AI users, but a process to create AX strategy planners who can design and execute a company's AX.

The automotive and parts manufacturing industry is a field where innovation is essential amid fierce global competition and complex supply chains. This course focuses on how AI technology can be applied in the field, explaining actual cases including production efficiency improvement, quality inspection automation, equipment predictive maintenance, and supply chain optimization. From the foundational understanding needed for AI implementation to strategies and risk management considerations for field application, this course covers topics step by step, providing knowledge and insights that practitioners and managers can immediately utilize.

In the era of AI and Big Data, are you utilizing your organization's data effectively? This course builds essential capabilities for reading and interpreting data, covering everything from the foundations of statistical thinking to the core principles of machine learning and deep learning. Grow into a practical leader capable of discovering patterns amidst volatility and making data-driven decisions.

Gain insights from DataOven CEO Ha Yongho, a first-generation data analyst who has demonstrated numerous insights and successes. This is a meetup video for data professionals, held on July 18, 2024.

This is a lecture where you can easily learn important mathematical concepts used in games. People who are well-versed in mathematics take this knowledge for granted, so they cannot teach it assuming you know nothing. I explain from a beginner's perspective, truly assuming you are a non-major.

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.

Starting with the question, "If I were on the Titanic, would I have survived?", this course completes a full-stack project where you develop an AI model to predict survival probabilities based on real data and deploy it as a web service. You will gain hands-on experience in the entire process of AI and web development, from deep learning modeling using PyTorch to building a backend server with FastAPI and implementing a user interface with Next.js.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

This course covers the content necessary to understand and create machine learning algorithms, specifically deep learning and neural networks, through linear algebra concepts. It provides a comprehensive explanation of linear algebra, including applications in probability, statistics, and optimization.

This course is designed to learn the fundamental theories and practical processes necessary for applying AI modeling and service planning to real-world work environments. Learners can acquire essential foundational knowledge to skillfully perform AI modeling projects and experience various practical procedures used in actual business settings. The course is specifically structured to provide step-by-step learning of the entire workflow from AI service environment analysis → goal setting → requirements analysis → service model design → scenario planning → utilization planning → execution plan development → performance evaluation planning. Through this approach, learners can develop systematic thinking and practical application skills for how to introduce and utilize AI from business and service planning perspectives, rather than limiting themselves to purely technical learning. Upon completing this course, learners will understand the overall planning and execution process of AI projects and will be able to strengthen their capabilities as AI service planners, PMs, and data-driven strategists in actual corporate environments.

This course aims to cover the latest Gemini API SDK (@google/genai) in a Node.js and TypeScript environment. You will learn how to use multimodal technology to process text, images, and video simultaneously. We will practice the entire process, from issuing an API key to project setup and model calling.