Generative AI Utilization Methods for Businessmen

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.

The AWS Certified AI Practitioner practice exam course is a core summary program for passing the certification. First, you'll quickly review the fundamental concepts of AI/ML and generative AI, and learn about the features and use cases of AWS's major AI services that frequently appear on the exam (e.g., SageMaker, Rekognition, Polly, etc.). Based on this, you'll practice determining which AI solutions are appropriate for various real-world problem scenarios, and also cover content related to responsible use of AI models and data security. Finally, you'll learn problem-solving strategies by question type and prepare for the exam through practice mock tests. Through this course, you can efficiently prepare for the exam in a short amount of time.

You will understand the present and future of AI technology, experience the testing process of AI models such as machine learning, and learn the role of a tester. You will comprehensively learn everything from establishing AI-based system test strategies to designing and executing test cases, and even methods for utilizing AI to support testing. This course is conducted based on ISTQB's AI Testing syllabus and provides an opportunity to perfectly prepare for the ISTQB AI Testing certification exam.

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!

You can transform your ideas into beautiful presentations in just a few minutes. No more spending hours struggling with slide design. With GAMMA's AI technology, you can dramatically improve your content creation process.

LLM and Agent, the fundamental technologies behind AI like ChatGPT. LLM is the technology that has come closest to AGI at this point in time. In this lecture, we explore LLM and Agent technologies from a general public perspective.

In an era of explosive data growth and crucial real-time processing, edge computing has established itself as an essential technology, no longer an option. This course is designed to systematically teach everything, from the core concepts of edge computing to deployment strategies for its application in real business environments. Through this course, students will firmly solidify the fundamental principles of edge computing and acquire practical knowledge and know-how applicable to real-world tasks and projects. Take the most certain first step towards becoming an edge computing expert, a core technology in the future IT environment!

Create high-quality AI videos with Midjourney&Runway. Director Kim Dong-ah, who collaborated with Samsung, will personally guide you step-by-step through the AI video production process based on a virtual brand.

Artificial Intelligence, AI, agents... They might seem grand, but once you try them, they're not as difficult as you think. That's why it's important to implement simple features yourself. Through practical projects that can actually be used at a company, you'll directly experience various use cases and learn how to utilize and apply AI agents.

This course is a hands-on masterclass that completely conquers the evolution of Diffusion Models through papers and code implementation. You'll learn the core models of generative AI, including DDPM (Denoising Diffusion Probabilistic Model) and DDIM, by studying the paper principles and implementing them directly. We analyze step-by-step the background of each model's emergence, mathematical formulations, network architectures (U-Net, VAE, Transformer), training processes (Noise Schedule, Denoising Step), and the ideas that led to performance improvements. Students will directly code all models using PyTorch, gaining not just paper comprehension but 'practical skills to reproduce and apply' them in real-world scenarios. Additionally, by comparing the differences between models and their developmental flow, you'll clearly understand how they expand and evolve. This course integrates theory, code, and practice into one comprehensive journey, providing researchers, developers, and creators alike with a systematic way to master the evolution of generative models. Beyond simply 'reading' papers, start your experience of 'understanding and recreating' through direct implementation now.

AI Utilization Master Guide for Java Developers! This course breaks the stereotype that AI is exclusive to Python, and grants your Java apps AI's eyes, ears, voice, and coding abilities. Grow into an AI architect based on OpenAI API. Directly develop various AI specialists such as AI writers, translators, artists, and coding partners, and create a multifunctional AI assistant that integrates core features like text summarization, sentiment analysis, image description, Java code generation, voice/text conversion, and image/subtitle generation. Learn to assign roles to AI through system messages and even content moderation for harmful content - this is a golden opportunity to maximize the competitiveness of your existing Java systems.

Based on over 100 deep learning training sessions, this course systematically organizes the core foundational theories that students found most challenging. The course connects mathematical intuition, model learning principles, and code implementation step by step in a way that even non-majors can understand, deeply covering the fundamental structure and operating principles of how AI models learn, rather than just library usage. This is a practical introductory course designed to help you grow into a skilled engineer who understands AI principles by implementing core deep learning foundational technologies such as gradient descent, loss functions, optimization, perceptrons, multilayer neural networks, and backpropagation through both formulas and code.

An introductory AI lecture tailored for those new to AI! Key concepts, from data to generative AI, are simply explained. The goal is practical understanding, enabling you to question and explain AI, not build it.

This course is ideal for those who want to focus on theory and context rather than practice, aiming to organize their knowledge of deep learning in depth and grasp the big picture. It makes the mathematical and statistical foundations of deep learning easy to understand and provides intuitive interpretations of key modern models—such as AutoEncoder, GAN, Transformer, and AlphaGo—from a developer's perspective.

This AI Literacy and Transformation Series (1) covers the following topics: 1. Data Center Cooling Innovation in the AI Era The Problem: The explosive power consumption of the latest AI chips (GB200, etc.) has pushed traditional air cooling methods to their limits, leading to AI performance degradation and massive operational cost issues. The Solution: Presents innovative liquid cooling technologies including Rear Door Heat Exchangers (RDCH), Direct-to-Chip (DTC) cooling, and immersion cooling. Additionally, provides strategies to simultaneously secure efficiency and sustainability through intelligent control systems that predict even leakage current and circular economy models that recycle waste heat for district heating. 2. The AI Paradox: Strengthening Critical Thinking to Awaken the Brain The Problem: The phenomenon of 'cognitive offloading'—outsourcing thinking to AI—is causing modern people's analytical thinking and literacy skills to rapidly deteriorate, creating a 'cognitive debt' problem. The Solution: Redefines AI not as a simple answer tool but as a tool for a 'cognitive gym'. Builds a system that makes you think first using critique, research, and synthesis agents, and strengthens metacognitive abilities as a 'helmsman' who determines direction rather than a passive passenger. 3. AI Memory Crisis: Next-Generation Architecture Design The Problem: The size of short-term memory storage (KV cache) required by AI is exploding, but expensive and limited-capacity HBM alone faces physical limitations in handling this data wave. The Solution: Presents a strategy for building a 'heterogeneous memory dream team' combining HBM, DRAM, CXL, etc. Provides solutions that dramatically lower corporate Total Cost of Ownership (TCO) by transferring smart system architecture design capabilities that balance capacity and cost rather than focusing on single component performance. 4. Palantir Ontology: Actionable Data Intelligence The Problem: Data is overflowing but fragmented, creating a 'data graveyard' problem where it fails to translate into actual decision-making and execution, blocking corporate innovation. The Solution: Gives data context and meaning through an 'Ontology' engine that maps physical assets to digital representations. Realizes data democratization where field personnel can directly command business without IT department assistance through 'Action Types (execution capabilities)' that immediately reflect analysis results into operational systems. 5. The Great Rewiring: AI Intelligence Stack Strategy The Problem: As the technology paradigm fundamentally shifts from the Internet era's 'deterministic computation' to the AI era's 'probabilistic prediction', existing business structures are being completely shaken. The Solution: Clarifies the reality of change through a '5-layer Intelligence Stack' map connecting semiconductors to applications. Provides a strategic decision-making framework for determining whether to Build or Buy AI capabilities, drawing a roadmap toward autonomous operating enterprises. 6. Forgetful AI: Maximizing Potential Through Feedback The Problem: AI's 'catastrophic forgetting' phenomenon where learning new information causes it to forget existing knowledge, and communication problems where unilateral commands prevent AI from performing properly. The Solution: Teaches 'coaching' and example-based dialogue methods instead of commanding AI. Transfers techniques for becoming a scientific training partner that enables AI to acquire new capabilities while maintaining existing knowledge through gradual feedback techniques that minimize behavioral change shock (KL divergence). 7. Engineering Financial Intelligence The Problem: Financial systems that remain stuck in past bookkeeping records and regulatory compliance cannot optimize capital allocation for an uncertain future. The Solution: Transforms into a 'dynamic scenario planning (war game)' system that computes thousands of variables in real-time using generative AI. Helps CFOs evolve beyond simple managers into strategic architects who design corporate value through 'Financial LLM' utilization methods that convert simple numbers into strategic signals.

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 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 explains the foundational principles of large language models like ChatGPT with a focus on theory.

Using block coding Entry, foster computational thinking and problem-solving skills, and through Entry AI tools, learn artificial intelligence concepts and practice simple machine learning.

Conquer everything about world-changing AI in just 4 lectures! Lecture 1. The True Identity of Artificial Intelligence AI is not magic. It's a digital brain created by analyzing human 'intelligence' like a mathematical formula! We thoroughly dissect the core structure of seemingly smart artificial intelligence. Lecture 2. Algorithm, AI's Creative (?) Thinking Method How does a pondering AI find answers? Experience firsthand the strategies of AI that plays Go, finds routes, and predicts your search results. Lecture 3. AI Hardware, The Engine That Turns Imagination Into Reality! The chip inside your smartphone is the heart of AI. We dissect everything from CPU, GPU, TPU, to even those unknown names. Lecture 4. The Great Clash of Machine Learning and Deep Learning! What on earth does it mean for AI to 'learn'? From classic rule-based AI to the currently hot ChatGPT! We trace the evolution of learning.