Shin Kyung-shik's Deep Learning Odyssey - Image Basics for Computer Vision

This lecture focuses on convolution, the most important operation in computer vision using deep learning.

This course provides a complete theoretical overview of the operations of convolutional and pooling layers, which are the core modules of Convolutional Neural Networks, and helps you develop implementation skills by building them yourself.

This course will help you grow into a deep learning-based computer vision expert needed in the field through in-depth theoretical explanations of Object Detection and Segmentation, along with practical examples at a level that can be immediately applied in the industry.

Learn the core principles and practical implementation of Classic RAG, Graph RAG, and Agentic RAG. Design a dynamic routing system that optimizes token efficiency and latency, and build a long-term memory system that combines GraphRAG’s relational network reasoning with Agentic RAG’s self-evaluation loop. This course develops advanced RAG architecture design skills that can be applied directly in real-world settings.

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.

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!

From core theories of Deep Learning and CNN to implementation methods of various CNN models, and practical Deep Learning development know-how through real-world problems, If you want to become a Deep Learning CNN technology expert, join us in this lecture :)

Let's acquire the Python coding skills necessary for artificial intelligence by learning the basics of the Python programming language!

Putting a neural network on an FPGA — it sounds familiar, but few people have actually done it. If you want to get ahead in capstone projects, competitions, or graduation projects, you need to go beyond training a model: you must be able to build and prove for yourself how its computations actually run inside a chip. This course helps you bridge that gap between theory and physical hardware. From reading the specifications to building a Python golden model, Machine Learning training, Verilog RTL design, and FPGA verification, you will complete a neural network accelerator (NPU) for recognizing MNIST digits written in real people’s handwriting from start to finish. From a Conv layer built 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 answer and the hardware output do not differ by even one bit (Bit-True)." The course also covers how to overcome that frustrating moment when the simulation runs perfectly, but the results are subtly different. We will walk through practical know-how that general deep learning textbooks do not cover, one step at a time — including integer quantization, array-axis ordering conversions, and preventing accumulator overflow. Drawing on the trial and error I have experienced through extensive work with RISC-V CPU design and FPGA Bit-True verification, I have built those lessons into this course so that you do not have to repeat them. By the end of the course, you will have a complete hardware project in hand and be able to say, "I designed it myself and proved it.&quot

Easy, Usable Computer Vision Projects: Step-by-step learning of practical CV projects. Boost skill with real examples.

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.

Hi Guys. Welcome to my Full AI Course 2026: ChatGPT, Google Gemini, Midjourney, Firefly. In my course you will learn everything about all AI Tools. I have created video lessons on every feature of these AI tools. You will get to see the real practical implementation of how to use every feature of this tools.

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.

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 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.

AI does the coding, I get the results! Maximize practical programming skills, achievable even for beginners!

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 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.

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.

Solve the point where 80% get stuck at makeathons. Complete Raspberry Pi · Computer Vision · LLM · 3D Design in 4 weeks! Achieve top rankings at makeathons with a demonstrable smart mirror PoC!