Deep Learning CNN Complete Guide - Pytorch Version

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 is designed to help you learn the core functions of FastAPI, as well as the entire process of web service development. Through this course, we will help you become a FastAPI expert developer needed in the field.

Through in-depth theoretical explanations of Kafka Connect and detailed practical training that can be used immediately in the field, we will help you grow into an expert in building data linkage and data pipelines based on Kafka Connect that are needed in the field.

Through detailed lectures and hands-on practice on the core elements of SQL, we will provide you with a solid foundation to grow into a SQL analysis expert.

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.

This course is a practical Airflow master program designed to help you understand "why Airflow works this way" and enable you to design and debug data pipelines on your own. It covers everything from Apache Airflow's core mechanisms to detailed theory and hands-on practice involving DAGs, Operators, Hooks, Scheduling, Timezones, Idempotency, and Templates.

By implementing various practical data analysis cases using SQL, you can simultaneously improve your data analysis and SQL utilization skills.

We will help you easily understand the core concepts of machine learning and acquire the ability to implement practical machine learning applications by moving away from theory-based machine learning courses.

It provides a key guide to understanding the internal mechanisms of Oracle DB architecture and growing as a performance tuning and performance analysis expert.

This course is designed to help you learn the use of ksqlDB and its core mechanisms through various hands-on exercises. After completing the course, you will be able to easily and quickly build a real-time streaming data analysis system based on Kafka.

From the core of Kafka to in-depth content on internal mechanisms, the course is structured so that even beginners can quickly reach the expert level through detailed theoretical explanations, hands-on practice, and practical Kafka application development practice.

If you want to be recognized as a machine learning expert in large-scale data environments—from understanding the core framework of Spark Machine Learning to SQL-based data processing through high-difficulty practical problems, and the ability to implement optimized machine learning models through business domain analysis—join this course.

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 :)

This course is designed to upgrade your skills as a practical machine learning development expert by implementing the machine learning model of the Home Credit Default Risk competition on Kaggle.

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 :)

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.

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

Many people use ChatGPT and frequently hear about recommendation systems and autonomous driving, but when asked to explain “why they work the way they do,” they often struggle to find the words. If you only follow coding courses, the code runs but the underlying principles don’t stick. If you only take math courses, you see the formulas but can’t connect them to AI. Overseas courses can feel intimidating because of the English, while introductory courses in Korea tend to focus heavily on hands-on practice. While building real models in AI competitions, running Everyone’s AI, and meeting thousands of learners, I repeatedly saw the same thing: the obstacle wasn’t how to use the tools, but the moment when functions, derivatives, vectors, and backpropagation became disconnected from one another. This course bridges that gap. From how functions define inputs and outputs, to how derivatives determine the direction of learning, and how backpropagation passes errors backward, it explains everything in one continuous flow—from the fundamentals of mathematics to neural networks and softmax. Rather than memorizing formulas or simply copying code, this course helps you understand “why” through equations and intuitive illustrations. For anyone who wants to truly understand AI, it will serve as a solid first step toward moving on to deep learning.

Are you feeling overwhelmed by practical application and interview preparation? Based on my industry experience, I will help you firmly grasp everything from CNN to DETR, with a strong focus on code.

We provide an easy understanding of the concepts and principles of generative AI from a humanities perspective, including literature, film, music, and history, while exploring the cultural impacts and life changes AI will bring. We offer practical insights and strategies on how individuals and companies should respond and prepare for the AI era.

Find your way through the sea of data and explore the future of artificial intelligence. This course explores the two massive technological trends of Big Data and Artificial Intelligence (AI), examining how they are bringing innovation to our lives and work.

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.

Through this lecture, you'll be able to learn about the AI graduate school admission process and preparation methods from start to finish! I'll also share some great tips to increase your chances of getting accepted!

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.

Do you feel stuck every time you have to write a report? Wasting time not knowing what to write, revising over and over because the logic isn't clear, and barely finishing right before the deadline—this is a routine many professionals and students repeat. You may have tried using ChatGPT, but it often stops at simply writing sentences for you. It might produce "plausible writing," but it doesn't create a "result-driven report." This lecture is different. We will show you how to use ChatGPT not just as a task-delegation tool, but as a strategic partner that amplifies your planning capabilities. ✔ How to clearly structure problem definitions ✔ How to apply frameworks to design logical flow ✔ How to strengthen persuasiveness based on data ✔ Strategies to upgrade to performance-oriented reports During the lecture, you will actually complete one of your own work reports, providing a practical experience that leads to immediate results rather than simple theory. Furthermore, by utilizing the latest features like Canvas and file upload analysis, we help you build AI literacy to stay ahead in the rapidly evolving generative AI environment. After taking this course: Your report writing time will decrease, Your logic will become more solid, And your results will become clearer. Experience the moment when report writing is no longer a burden, but a weapon that proves your capabilities.

This course allows you to build an AI portfolio by cloning the Amenity Detection project from the Airbnb Data Science team—a prime example of TensorFlow utilization—and documenting the entire process on a GitHub blog.

A general education lecture for the public to shed the vague fear of artificial intelligence. Through concrete examples, we learn what AI learning truly means. Also, via specific examples, we examine AI's 'brain structure' and how it thinks.

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."

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.

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.

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!

This is a practical course where even beginners can learn how to quickly create PDF and Notion templates using AI and generate passive income by selling them on platforms like Etsy, Gumroad, and the Notion Marketplace. You will master everything at once, from market research and production to creating product detail pages, uploading, and sales strategies.

For those of you who have completed intermediate SwiftUI, it is now time to integrate AI into your apps. Complete an on-device AI app that runs locally without an internet connection using Apple Intelligence and the FoundationModels framework.

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.