Deep Learning with Excel - Stacking Deep

[Lecture Summary: From Pixels to Practice, Building a Business Card Recognition System] * **Pre-processing:** We learned how to remove noise and extract the 'skeleton' of a business card using Gaussian Blur and Canny Edge detection. * **Hough Transform:** We went through an "audition" process to select the real 'borders' from tens of thousands of points using the $(\rho, \theta)$ voting system. * **Geometric Reasoning:** By solving complex linear equations through substitution and matrices, we precisely targeted the four corners $(x, y)$ of the business card. * **Data Sorting & Filtering:** We mastered data refinement techniques, such as removing overlapping lines and labeling the found points in the order of 'top-left, top-right, bottom-right, bottom-left.' * **The Magic of Transformation (Perspective):** We concluded with practical techniques for straightening tilted images or, conversely, compositing an image into a frame of your choice. 📢 "Does looking at formulas give you a headache? It was the same for me." When I first took on an image processing project in the field, what bothered me most were the screens full of $\sin$ and $\cos$ formulas. Textbooks were full of nothing but equations, and nowhere provided answers to practical problems like, **"Why can't this find the business card in my code?"** Through this lecture, I want to help you solve three chronic problems you will encounter: 1. **"The problem where math feels like an alien language"** → Solved through [The Power of Analogy]. Instead of simply memorizing linear equations, I help you understand them through **'lasers fired from the origin'** and **'shadow play.'** Once you understand the principles, the code follows naturally. I translate formulas into the 'easiest language' that I discovered after countless trials and errors. 2. **"The problem where you know the theory but can't apply it"** → Solved through [Data Auditions]. Why are there 10 borders on my business card? Why is the order of the corners scrambled? I have revealed all my debugging know-how on how to filter and sort the 'messy data' you inevitably encounter in practice. The audition logic for separating the wheat from the chaff will take your project to the next level. 3. **"The problem where the result is boring"** → Finished with [Interactive Composition]. This isn't a lecture that ends with just looking at black-and-white edges. The moment you tuck the business card you created into a 'Hall of Fame frame' that you clicked yourself with a mouse, you will experience **'the magic of manipulating space'** beyond simple coding. "Computer vision is not about math; it's the process of gaining a new set of eyes to see the world." I want to pass on to you that thrilling 'A-ha!' moment I realized in the field. You can close the complex theory books now. Let's paint images freely with the brush of code together!

Python + xlwings Work Automation Course Summary 1. Basic Section: Excel Control Fundamentals Cell Handling / Format Application / Image & Chart Insertion 2. Application Section: Automation Scenarios Automatic Email Sending / Report Summary Automation (LLM) / Image Classification Tasks (Teachable Machine)

This lecture examines the entire process of generating a photo mosaic from an algorithmic perspective. The input image is reduced to a set of representative colors matching the number of tiles through K-means clustering, then the Hungarian algorithm is applied to optimally match each color with a tile in a 1:1 correspondence. Through this, you will understand how image processing and combinatorial optimization algorithms combine to create a single visual output.

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

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.

Implementing deep learning basics with Excel to deeply understand their meaning! Learn how image data is processed!

Implement Deep Learning Basics in Excel for Deeper Understanding!

Let's implement a deep learning framework directly with Excel! Let's solve supervised learning/unsupervised learning/reinforcement learning with the implemented framework!

Let's learn the principles of deep learning by visualizing them with Excel.

Pre-Trained Model (VGG16) / CAM / Grad-CAM / YOLO / STYLE TRANSFER / PIX2PIX / CYCLEGAN Implementing Stable Diffusion Directly

Hot image generation model! Let's understand it by implementing it from the basics! Let's implement AUTO ENCODER / VAE / GAN / DIFFUSION together!

Implementing deep learning basics with Excel to deeply understand their meaning! Learn how image data is processed!

Implement Deep Learning Basics in Excel for Deeper Understanding!

Let's implement a deep learning framework directly with Excel! Let's solve supervised learning/unsupervised learning/reinforcement learning with the implemented framework!

From Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder based MarianMT translation model, and even Vision Transformer, you'll learn Transformer inside and out by implementing them directly in code.

This course covers everything from the basics to hands-on practice of AI platforms, and is a program where you can learn AI image generation technology. Learners can understand the fundamental concepts of AI and develop the ability to generate AI images using various tools and technologies. Through this course, you can enhance your understanding of the AI field and learn how to directly generate AI images in real-world applications.

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!

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.

This course starts with the question "If I had been on the Titanic, could I have survived?" and completes a full-stack project that develops an AI model to predict survival probability based on actual data and serves it as a web service. You'll practice the entire process of AI and web development, from deep learning modeling using PyTorch, building a backend server with FastAPI, to implementing a user interface with Next.js.

This course explains the basic concepts of LLM and GPT in an easy-to-understand way for everyone. Students will build their own chatbot using the GPT API and deploy it on the web using Streamlit. By experiencing everything from the basics to hands-on practice, you can build a solid foundation for your first steps in utilizing AI.

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 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.

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 :)

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.

The electronics and semiconductor industry is a field where data-driven management and innovation are particularly crucial due to ultra-precision processes and complex supply chains. This course covers practical strategies that can be directly applied in electronics and semiconductor manufacturing, including defect detection, process optimization, predictive maintenance, and supply chain management using AI technology. Along with real-world cases from global companies, the course also presents low-cost, high-efficiency AI implementation methods that small and medium-sized enterprises can realistically utilize. Through this, students will be able to understand and apply AI-based manufacturing strategies that not only improve productivity and reduce costs, but also build future competitiveness.

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.

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.