Advanced concepts for Deep learning

After using it for 14 days starting immediately upon release, here are the reasons why I recommend Claude Code (AI Coding) to everyone—from job seekers to industry professionals—as something they can apply right away.

Is this kind of introduction even important right now? Use the DeepSeek R1 model right away.

We help you accurately intuit the concepts of the theories that form the basis of deep learning.

We will learn about Q-learning and Deep Q-learning, and have time to implement reinforcement learning in R. We will cover the entire reinforcement learning content, from Deep Q-network to Self-imitation learning and Random Network Distillation.

We help you accurately intuit the concepts of the theories that form the basis of deep learning.

See exam types and trends at a glance! Strengthen your practical skills with approximately 100 questions. The perfect preparation for the AWS AI Practitioner exam, allowing for systematic study even when preparing on your own.

Based on the experience of wondering how facial recognition works in offline environments on-site, this course will guide you through building the basic structure of a facial recognition-based access and attendance management system using React + Electron and Python's FastAPI-based DeepFace. Rather than focusing on perfection, we focus on understanding the operating principles and laying the foundation for you to expand into your own personal projects.

Building a Full-Stack System Without Coding: Complete the entire pipeline from data collection to AI/ML analysis and web dashboards using only Korean prompts (Vibe Coding). Owning a 100% Automated Pipeline: Secure an independent stock analysis tool that sends S&P 500 screening and investment opinions via Telegram every morning. Acquiring a Practical Data-Based Investment Framework: Master institutional-level data analysis and risk management capabilities based on source code currently in actual operation.

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.

When you study machine learning, you learn how to improve accuracy. But the moment you try to deploy that model onto an actual chip, you are left with questions no one ever taught you to ask: How many multiplications does this model require? What does it need to remember, and how much? Can the target chip specifications handle these demands? Machine learning textbooks focus on accuracy, while semiconductor textbooks focus on circuits, yet resources bridging the two are scarce. I repeatedly encountered this gap while implementing RISC-V CPUs and NPUs on FPGAs—facing situations where the model with the highest accuracy simply wouldn't fit on the board. In this course, you will build six algorithms from scratch in Python, measuring the number of multiplications, comparisons, memory usage, and accuracy for each. You will then translate these metrics into actual FPGA board resources. While knowing the number of multiplications alone doesn't give you a sense of scale, expressing it as a percentage of the board's resources makes it easy to assess. After completing nine labs and compiling the six models into a single table, an unexpected picture emerges. We spent nine sessions counting multiplications, only to find that multiplications weren't the actual bottleneck. From there, we move on to quantizing floating-point numbers to integers, managing bit widths, and even establishing criteria for grading circuits. An FPGA board is not required. The course can be completed using Python alone, with the board serving merely as a reference for translating the measured metrics.

"The Fourth Industrial Revolution, AI factories... The terms are familiar, but can't you visualize the structure?" Smart Factory, often called the future of manufacturing. While related articles pour out and it appears as a required competency in job postings, the barrier feels far too high for non-majors. "Did you give up because of abbreviations that sound like alien languages, such as PLC, MES, or ERP?" "Are you afraid to even start because you lack knowledge in coding or engineering?" Don't worry. This lecture deals with 'structure,' not 'engineering.' Instead of complex text, we will help you complete a full map of the smart factory in your head using intuitive drawings and diagrams, just like assembling Lego blocks.

In the era of smart factories and autonomous driving, is our production line truly safe? The remarkable advancement of Artificial Intelligence (AI) has brought innovative productivity and efficiency to the automotive manufacturing industry, but it is simultaneously creating unprecedented security threats to the Operational Technology (OT) environment within factories. This lecture breaks down "OT Security"—which can often feel vague and difficult—in a clear and easy-to-understand manner through vivid, real-world examples from the automotive industry.

This lecture is designed for job seekers preparing for Evaluation and Analysis roles in Samsung Electronics' DS Division (Memory, S.LSI, Foundry, TSP, AVP) and PE, AE, and Solution PE roles at SK hynix, who are struggling with the differences between business units and job functions. You will learn how to categorize job titles by company based on the actual workflow of Product Evaluation roles and how to translate your major-related experiences into professional terminology suitable for resumes and interviews. We will help you establish an application strategy by connecting experiences in Analog circuit design, Verilog/FPGA, process experiments, and data analysis with key industry terms such as Yield, Wafer Map, Shmoo Plot, and Failure Analysis.

This course covers the entire process of evolving ChatGPT from a simple conversational AI into an intelligent AI Agent that performs real-world tasks. By utilizing the three core frameworks—LangChain, LangGraph, and LangSmith—you will build AI Agents that can be used immediately in practice.

1. Python coding study to obtain the AI certification, AICE Associate 2. Step-by-step Python coding study from basics to applications 3. Lectures by Professor Joongheon Kim of Korea University, a scholar in the field of AI

Build your own powerful stock price analysis model that analyzes over 40 types of economic indicators and stock price data using AI to predict not only the S&P 500 and QQQ ETFs but also individual stocks!

Understand the basic concepts and latest trends of big data, and through technical understanding of big data analysis techniques, extract data patterns to present methods for business application. Through big data, you can create new businesses and develop insights into future business environments.

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 is designed to teach the entire process of 'AI Training Data Construction,' which is essential for successfully executing AI modeling projects. Learners will not simply remain at the theoretical knowledge level, but will experience step-by-step the data construction processes required in actual practice. Specifically, the course covers the entire process from AI training data planning → acquisition → storage → preprocessing → labeling → pseudonymization and transformation through hands-on practice, structured to develop practical skills that can be immediately applied in the field. Additionally, learners will systematically study the most important data management capabilities in AI projects, such as data quality management and security issues, personal information de-identification processing, and dataset optimization. Through this, learners can grow from simple data collectors into data specialists who can proactively plan and execute AI projects. 👉 Through this course, learners will understand how to construct high-quality training data that determines AI model performance and acquire practical capabilities to effectively apply this in real work environments.

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.

Putting a neural network on an FPGA—the phrase is familiar, but few have actually done it. If you want to stay a step ahead in capstone projects, competitions, or graduation projects, you must be able to go beyond just training a model and actually build and prove how those computations run inside a real chip. This course bridges that exact gap between theory and physical hardware. You will complete a neural network accelerator (NPU) for MNIST digit recognition from start to finish—from reading specifications to Python golden models, Verilog RTL design, and FPGA verification. From Conv layers made with line buffers and parallel MACs, through ReLU, MaxPool, and Flatten, to fully connected layers and Argmax—you will integrate nine blocks into a single chip and prove for yourself that "the Python reference and the hardware output do not differ by even a single bit (Bit-True)." I have even included how to overcome those frustrating moments when the simulation runs fine but the results are subtly different. I will guide you through practical know-how that general deep learning textbooks don't cover, such as integer quantization, array axis reordering, and accumulator overflow prevention. I have distilled the trial and error I experienced over years of RISC-V CPU design and FPGA Bit-True verification into this course so that you don't have to repeat them. By the end of the class, you will have a completed hardware project that allows you to say, "I designed and verified it myself."

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

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.