Deep Learning CNN Complete Guide - Pytorch Version

This course will help you become a deep learning-based computer vision expert who is needed in the field by providing a deep theoretical explanation of object detection and segmentation and practical examples that can be used immediately in the field.

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.

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 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.

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 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 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.

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.

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

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

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 :)

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

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 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 :)

Data Science(DS) & Artificial Intelligence(AI) job role analysis, plus a learning & employment roadmap for building a career in these roles.

This is a lecture to build an AI portfolio by doing a clone project of airbnb data science team's Amenity Detection project, an excellent TensorFlow utilization case, and organizing the experience on a GitHub blog.

Explaining how to connect to a Python-based AI web server using FastAPI from a Spring Boot project.

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

This is a course where you learn the basic calculus and PyTorch's Autograd functionality needed to get started with deep learning.

You will learn how to process images and videos with OpenCV, and how to identify and recognize faces and various objects using deep learning. This is a course where you will learn fun projects such as drowsiness prevention, age and gender identification, counting the number of people passing by, face tracking, and YOLO that finds bad apples and tangerines, along with theories.

This course is designed to help those who want to deeply organize deep learning and grasp the big picture by focusing on theory and context rather than hands-on practice. You can easily understand the mathematical and statistical background that forms the foundation of deep learning, and gain an intuitive interpretation from a developer's perspective of the principles behind major modern deep learning models such as AutoEncoder, GAN, Transformer, and AlphaGo.

The AWS Certified AI Practitioner practice exam problem-solving course is a core summary course for certification success. First, you will quickly review the basic concepts of AI/ML and generative AI, and learn the functionalities and use cases of key AWS AI services that frequently appear on the exam (e.g., SageMaker, Rekognition, Polly, etc.). Based on this, you will practice determining which AI solution is suitable for various real-world problem situations, and also cover topics related to the responsible use of AI models and data security. Finally, you will learn problem-solving strategies for each type of past exam question and prepare for the exam through practical mock tests. Through this course, you can efficiently prepare for the exam in a short period.

This course is designed to simultaneously develop both fundamental theory and practical application skills needed to proficiently execute AI modeling projects in real-world settings. Learners will understand the overall flow of AI projects and experience step-by-step processes through hands-on practice: problem definition → model design → data acquisition and preprocessing → feature extraction → model training and validation. Additionally, by learning how to select and manage optimal AI models in various situations, learners can build practical AI capabilities that can be applied to actual business environments. Through this course, learners can systematically understand the entire AI modeling project process and strengthen their data-driven problem-solving abilities based on this foundation.

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.

Explains the basic principles of large language models like ChatGPT, focusing on theory.

This course covers the entire process of evolving ChatGPT from a simple conversational AI to an intelligent AI Agent that performs actual work tasks. Using three core frameworks - LangChain, LangGraph, and LangSmith - you'll directly create AI Agents that can be immediately used in real-world applications.

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.

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!

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.

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.

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

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.