Smart Mirror: Raspberry Pi, VISION, and LLM All at Once

Build a local LLM with RAG-based QA system in just 2 hours of practice using Docker + ollama + R2R framework. (Suitable for in-house/internal QA systems, personal portfolios, AI-powered work capabilities, and even commercial services)

It goes without saying that AI artificial intelligence is the trend. But what can you use machine learning and deep learning for? Now, let's learn useful Internet of Things IoT Computer Vision projects that are used in daily life.

What to Expect from This Course Welcome to LangGraph in Action, your ultimate guide to mastering the design and deployment of advanced AI agents using LangGraph. In this course, you’ll explore the fundamentals of building modular, scalable, and production-ready agents, all with a hands-on approach. From understanding the basics of LangGraph’s state-based design to creating a full-stack application, you’ll gain the skills needed to bring AI agents to life. Course Highlights State-Based Design: Dive into LangGraph’s core philosophy of nodes and edges to create structured, maintainable agents. Memory Management: Explore short-term memory with checkpointers and long-term memory with the Store object to enable agents that adapt and learn. Advanced Workflows: Build human-in-the-loop systems, implement parallel execution, and master multi-agent patterns. Production-Ready Development: Learn asynchronous operations, subgraphs, and create full-stack applications using FastAPI and Docker. By the end of the course, you’ll not only have a strong theoretical understanding but also the practical skills to deploy AI agents anywhere, entirely with open-source tools. Whether you're a developer aiming to stay ahead of the curve or a seasoned engineer looking to expand your AI toolkit, this course equips you for the rapidly growing field of AI agents. With the increasing adoption of AI agents in real-world applications, this course ensures you're prepared to design, build, and deploy advanced systems that solve practical challenges. Let’s start building and shaping the future of AI together!

RAG (Retrieval-Augmented Generation) from theory to the latest multimodal and agent-based RAG! This is a hands-on lecture designed to be understandable even for non-majors. From paper reviews to practical code implementation, it's designed so that even those encountering RAG for the first time can easily follow along.

This is a hands-on course where you'll build an AI-based autonomous driving car using Raspberry Pi and various sensors.

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

LLM: A pro quickly delivers core content, from essentials to practical tips!

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.

We explore methods for creating an AI judge by combining multiple agent frameworks such as CrewAI, LangChain, and AutoGen that enable the implementation of LLM-based multi-agent technology, along with knowledge graphs and reasoning systems.

Create a program using the GPT API and even generate images and videos!

Develop various features using Spring WebFlux. This course will teach you WebFlux's essential concepts and practical implementation.

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.

Hackathon and Project Introduction, Now Start with Arduino, Python, and Flask. Let's build a framework for hackathons and projects by creating cool projects together.

We implement the latest Transformer-based PixArt and lightweight adaptation SANA step by step from theory to code. Building on DDPM·DDIM·LDM·DiT covered in Parts I·II, we complete hands-on practice including text encoder integration, samplers (DDIM/ODE), v-prediction/CFG tuning, and small-scale data style fine-tuning.

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

Hot image generation model! Let's understand it by implementing it from the basics! Let's implement AUTO ENCODER / VAE / GAN / DIFFUSION together!

The shipbuilding and maritime industry is a representative field of traditional heavy industry, but it is securing new competitiveness through the combination of digital transformation and artificial intelligence (AI) technology. This course is structured around specific application cases actually used in the field, such as AI-based shipyard production management, ship design automation, maritime logistics optimization, and predictive maintenance system construction. With a field-centered approach, you can understand not just simple theory but **'how AI actually works in shipyards'**. Additionally, it covers data infrastructure construction, model training, and constraints specific to the marine environment that must be considered during the AI technology implementation phase.

This course is a specialized program that covers practical application strategies for utilizing artificial intelligence (AI) in the construction and civil engineering fields. In the construction industry where digital transformation is accelerating, AI is driving innovation across design, construction, safety, and quality management. This course focuses on learning practical application cases that can be utilized in actual field sites, including smart construction, digital twins, AI-based equipment control, safety monitoring, and quality verification.

The goal is to learn complex GPT API and Python in the easiest and most hands-on way possible. Using the latest ChatGPT API, we'll develop a YouTube video summarization AI and implement it as a web application using Streamlit.

Automate Creating YouTube Shorts with AI!! Learn from scratch how to automatically create YouTube Shorts with n8n. Build a complete workflow that runs from text→image/music→video creation→upload all at once. Practice distinguishing between test mode/production mode and tips for saving costs. Complete 3 ready-to-use automation templates by the end of the course. Learn step-by-step from node placement so you can follow along even without coding knowledge. Let's automate AI YouTube Shorts production together!