Practical PyTorch Computer Vision: A Complete Guide from CNN to the Latest DETR

This is a lecture that studies Vision Transformer, one of the latest deep learning technologies, and implements a paper using Pytorch. Come experience the new future of the vision field with me!

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 is an introductory course on OpenCV using C#. It mainly covers how to use functions, how function arguments affect results, etc., rather than theoretical elements.

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 has been newly designed to help you gradually learn the mathematics and theory needed to understand deep learning, PyTorch-based implementation, transfer learning, and the core GPT Transformer, based on the instructor’s experience of struggling when first learning deep learning.

Build a solid theoretical foundation of images essential before learning deep learning-based computer vision. Understand the essence of image data, such as pixels, resolution, and color representation, and cultivate practical programming skills to read, transform, and preprocess images using Python.

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.

Many people have learned FPGA and Verilog but get stuck at the question, "So, how do I actually build an AI accelerator?" This course resolves that curiosity. Using just a single entry-level FPGA and free tools, you will start from a hand-designed 3×3 kernel, train a CNN yourself, quantize the trained weights to INT8 to load onto the hardware, and verify that the results are Bit-True—not deviating by even a single bit from the Python model. As a professor with 30 years of experience in the semiconductor industry, I teach only verified content as 'working circuits' on actual FPGAs. By gaining the experience of designing and proving it yourself rather than just learning theory, you will acquire practical skills that are recognized in the fabless design and AI semiconductor fields.

Let's create a web app that uploads an image and uses OpenCV to process it and detect faces.

AI Artificial Intelligence, it goes without saying that mobile is the trend. But how do you learn machine learning and deep learning and use them on mobile? Now, let's learn useful mobile Computer Vision projects that are used in daily life.

This is a comprehensive machine learning project course that learns various useful machine learning regression and classification projects along with theory using Python and TensorFlow 2 based on Boston Marathon big data.

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

You will learn about Transformers inside out by directly implementing everything from Multi-Head Attention to the Original Transformer model, BERT, the Encoder-Decoder-based MarianMT translation model, and the Vision Transformer in code.

This course covers the theory and practical examples of LLMs, based on <Fundamentals of Large Language Models, Quickly!> (Insight, 2025).

This course consists of 47 lessons, totaling approximately 39 hours, in which you build deep learning from the ground up with PyTorch. There are many deep learning courses, but most fall into one of two categories. Either they skim only the basics of using libraries, leaving you unsure why the code is written the way it is, or they start with equations and leave you giving up halfway through. This course takes a different approach. First, you create a result; then you dig into what happened under the hood. The course begins with pretrained models. You will run image classification, try inpainting with Stable Diffusion, and even practice image captioning with BLIP. Because you produce tangible results from the very first lesson, you gain the motivation to learn the theory that follows. Next comes the core of this course. Tensors are covered over seven lessons. In addition to indexing and broadcasting, you will explore memory structures such as storage and strides, memory contiguity management, GPU operations, and conversions between tensors and NumPy arrays. It is rare to find a Korean-language course that covers tensors in this much depth. Once you understand this part, you will read all subsequent code differently. You will then learn preprocessing that converts real-world data into tensors. The course covers different data types, including image color channels, tabular data and one-hot encoding, time series, and text embeddings. Moving on to the principles of training, you will implement loss functions and gradient descent yourself using numerical differentiation, and then see how Autograd automates the process. Only those who have built it themselves can truly appreciate the convenience of automatic differentiation. The second half focuses on neural network architectures. You will build deep neural networks with nn.Module and classify CIFAR-10, encounter the limitations of fully connected neural networks, and naturally arrive at why CNNs are necessary. Finally, you will move on to text generation, covering everything from bigram models to embeddings and MLP-based generative models, and then the principles of Attention’s Query, Key, and Value. If you know how to use Python and want to properly understand deep learning, what you will take away from this course is not example code, but the ability to read and understand the structure of any model you encounter.