Maritime 4.0: Innovation Driven by AI, Data, and Cyber Security

The order in which subjects are approached may vary depending on the student's major, interests, and learning goals. However, generally, taking courses in the following sequence allows for efficient learning. (Source: Sungkyunkwan University GSIC ) 1. Basic Courses First, it is essential to understand fundamental theories and basic concepts. The following courses help build foundational knowledge: Introduction to Digital Forensics (FSI5056) : Establishes the basics of digital forensics and teaches various methods for collecting and analyzing digital evidence. Korea University Graduate School of Information Security Introduction to Cryptography (GSIS001) : Covers the fundamental principles and applications of encryption technologies for data protection. Life Coding Database (GSID003) : Introduces key concepts and practical skills for data storage and management. Operating Systems (GSID021) : Helps understand the structure and functions of operating systems, which are the core of com...

I have resumed engineering and modeling tasks , which I had temporarily set aside to focus on project management and new business planning . For a project that involves optimizing (tuning) individual models for image and text processing , as well as integrating multiple models —a skill I honed during my Ph.D. program—I now require a complex and well-structured work environment . To ensure optimal efficiency under the given constraints , I plan to set up, refine, and document the work environment for future reference. "If I had eight hours to chop down a tree, I would spend six hours sharpening the axe." – Abraham Lincoln (1861–1865), the 16th President of the United States Review of Work Environment 1. Managing Work on an Unstable Server & Data Backup ✅ Backup & Record Management: Using SFTP to store and synchronize data in a structured manner. ✅ GitHub Usage: Limited to baseline model development and version control for key components. ✅ Code & Data Managem...

Shifting Perceptions and Expectations of AI from Companies The way companies perceive and expect from AI is evolving. Until now, aside from cutting-edge research and product development , the role of AI engineers and data scientists in enterprises can generally be categorized as follows: Image Processing : Companies have widely utilized CNN-based models for object recognition and comparison (performance improvement). To overcome data scarcity, techniques like few-shot learning have been applied in areas such as product search, similar image/product recommendation, and design generation . Text Processing : Enterprises have leveraged various Transformer-based models trained on proprietary datasets for customer intent recognition and sentiment analysis . Data-Driven Decision Support : Companies often assume they possess sufficient data, but in many cases, they lack the necessary datasets. AI teams analyze available data, form hypotheses , and generate insightful reports to support an...

Subject: Natural Language Processing (NLP) Through my work in implementing AI chatbots and taking the Natural Language Processing (NLP) course, I realized that since the introduction of the "Attention Is All You Need" paper and the breakthrough models like "Transformer" , "BERT" , and "GPT-2" , there has been a significant increase in both corporate and academic interest in dialog systems such as AI chatbots and customer support services , with NLP technologies at their core. This shift can be attributed to the advancements in NLP technologies , which have reached a point where natural language services are widely accepted without significant resistance . In this context, I want to explore what Natural Language Processing (NLP) is, why the interest in it has grown rapidly , and what technologies have made this possible . Additionally, I will present the requirements and methods for developing AI services based on NLP technology . Approach...

  reference - http://hunkim.github.io/ml/ 모두를 위한 머신러닝/딥러닝 강의 -   홍콩과기대 김성훈- 시즌 1 - 딥러닝의 기본 (TF 1.X lab 완료!)  비디오 리스트 수업에 사용하는 코드  https://github.com/hunkim/DeepLearningZeroToAll 수업의 개요  비디오    슬라이드  머신러닝의 개념과 용어  비디오   (TensorFlow의 기본  Lab 비디오  )  강의 슬라이드    Lab 슬라이드  Linear Regression 의 개념  비디오   (TensorFlow 로 구현  Lab 비디오  )  강의 슬라이드    Lab 슬라이드  Linear Regression cost함수 최소화  비디오   (TensorFlow 로 구현  Lab 비디오  )  강의 슬라이드    Lab 슬라이드  여러개의 입력(feature)의 Linear Regression  비디오   (TensorFlow 로 구현  Lab1 비디오  ) (파일 데이타 로딩  Lab2 비디오  )  강의 슬라이드    Lab 슬라이드  Logistic (Regression) Classification  강의 슬라이드    실습 슬라이드  Hypothesis 함수 소개  비디오  Cost 함수 소개  비디오  TensorFlow 에서의 구현  비디오  Softmax Regression (Multinomial Logist...