AI-Driven Practical Application Strategies in Manufacturing: Chemical and Fine Chemical Industries

Learn how to build deep learning models yourself using intuitive and Pythonic PyTorch. This reflects the latest PyTorch version.

This is a lecture on implementing various artificial neural networks using PyTorch, a deep learning framework with very high utility in the field of artificial intelligence.

This course covers everything from the basic concepts of recommendation systems to the principles of applying deep learning. Develop your practical skills for recommendation service development by learning various recommendation algorithms, including collaborative filtering, content-based filtering, and hybrid recommendation systems!

"AI Team Members Do It All" is a hands-on online course where you learn to use AI not just as a simple automation tool, but as 'AI team members' who work alongside you in real tasks. This course is structured around common work flows in practice, including market research, proposal writing, strategy report creation, brand content visualization, project promotion and operations, and follow-up management. Students will complete actual work deliverables by deploying various AI tools such as ChatGPT, Perplexity, Copilot, Canva, Gamma, Miricanvas, and Bru as planning partners, strategic research partners, design partners, and operational support partners respectively. Over 30 sessions, you'll learn step-by-step from basic perspectives on working with AI to practical prompt writing, market research, proposal and report writing, brand content creation, and project operations. By following the course, you can develop AI collaboration capabilities that enable you to work like an entire team on your own.

Textbooks teach the modules separately, but the real world is different. An active SAP center director builds a hypothetical dumpling factory from start to finish and shows, through actual implementation project screens, how FI, CO, SD, MM, and PP really connect and work together.

Sensational video content created with just a single line of text! A quick and easy introduction to SNS content creation starting with Kling AI

Diagnose the future of my work and learn how to grow together with AI What we need now is the power to read the future of my work! In the midst of industrial restructuring and technological transformation trends, we'll specifically tell you from a job function perspective what changes are coming to your work now and how you can prepare for them.

We will provide information on 3D spatial information data acquisition and mapping methods using drones, LiDAR, etc., and data visualization tools and techniques based on the application areas of 3D spatial information data, incorporating various case studies.

3 projects that compress 20 carefully selected concepts will build 80% of your n8n skills.

Learn to use AI for work by reviewing business examples, writing prompts, and checking the results. Learn how to divide tasks into steps to delegate to AI and steps for you to review, enter materials and make requests within the scope permitted by your company, and compare the results of summaries, reports, meeting minutes, emails, and numerical tasks against the original materials.

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."

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

"We need to do DX/AX." It's always serious in the meeting room, but when you actually get down to the field, don't you feel lost about what to do first and how to do it? You've done a few PoCs, but they never transition to regular operations You bought solutions and equipment, but they're treated as cumbersome tools on-site There's a lot of talk about data and systems, but you can't figure out how to apply them to your current line or process There's no dedicated TFT or team, so you're a practitioner who has to handle both your main job and DX/AX... This course was created specifically for people like you.

It doesn’t stop at following along to create a sample game. Using ChatGPT- and Codex-based vibe coding, you’ll learn the entire process through a real launch project: from game planning → development → mobile builds → AdMob ad integration → publishing on ONE store, Google Play, and the App Store.

There may be people who have never taken my lecture, but no one has taken it only once. After teaching in person for so long, I have finally had the opportunity to create this online course. Since the commercialization of AI, I have produced and researched countless pieces of AI content. Based on that knowledge and experience, I have built a logical philosophy of my own and organized it into concepts that are easy for anyone to understand. For those who have been generating AI images without considering the underlying concepts, I will instill a proper foundation in you. Once you take this course, you will be able to create any AI image in the future. Have you never created an AI image before? That’s perfectly fine. You don't need any prior experience in AI image generation. By simply learning and practicing through this lecture, you can become an expert in AI content creation.

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.

Essential certification for the AI era, MS AI-900! 🚀 Did you know? ChatGPT operates exclusively on Microsoft Azure! If you want to properly utilize Microsoft Azure OpenAI, which exclusively provides the latest GPT models, now is the perfect opportunity. Grow into an Azure AI services expert through the AI-900 course and master enterprise-grade AI solutions that companies trust. Secure global official certification and enhance your competitiveness as a true AI professional!

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.

Even if AI quickly generates an SQL draft, people must judge the table structure, relationships, performance, and data consistency. Using MySQL and DBeaver, you will gain hands-on experience with everything from tables, columns, data types, PKs, and FKs to DDL, database modeling, SQL fundamentals, indexes and EXPLAIN, and transactions, learning the standards for reading and validating AI-generated results.

This course is an introductory program designed so that learners who are new to AI technology can easily understand and follow along. Through the lectures, you will learn the basic concepts and usage methods of AI platforms, and conduct hands-on practice directly generating images using various AI tools. Upon completing this course, you will become much closer to AI and acquire the capability to create AI images that can visually implement creative ideas.