Complete Analysis of the Solar Open 2 Architecture

Many people use ChatGPT and frequently hear news about recommendation systems or autonomous driving, but they find themselves at a loss for words when trying to explain "why it works this way." If you only follow coding lectures, the code runs but the principles remain a mystery; if you only take math lectures, you see the formulas but can't connect them to AI. Overseas lectures are burdened by the language barrier, while domestic introductory courses often lean too heavily on practice alone. I have seen the same scene repeated over and over while building actual models in AI competitions and meeting thousands of learners while running "Everyone's AI." The point where people get stuck isn't how to use the tools, but the moment when functions, derivatives, vectors, and backpropagation feel disconnected from one another. This course connects those dots. From the basics of mathematics to neural networks and softmax, it explains in a single flow how functions define inputs and outputs, how derivatives determine the direction of learning, and how backpropagation transmits errors. This is a class designed to make you understand the "why" through formulas and intuitive illustrations, rather than just memorizing equations or copying code. For those who want to truly understand AI, this will be a solid first step toward your next level in deep learning.

This is an introductory course where you learn the core concepts of Claude Code through a 3D office analogy. From hiring a single robot in an empty office, giving it a brain and a heart, equipping it with tools, permissions, and memory, to eventually having multiple robots work together as a team—each concept unfolds as an office scene. You will master every concept by interacting with the digital course materials and complete your learning through hands-on practice on your own computer.

Now, your AI is no longer in the cloud. It runs directly inside your laptop. Using LM Studio and Ollama, we will show you how to build a Private AI environment that runs entirely locally— from data processing and document analysis to code generation. Without security concerns, without costs, and even faster— Move beyond simply "using" AI and start "truly mastering" it.

Stop paying for student record design. I will reveal all the consulting secrets used directly by an admissions consultant with 30 years of experience.

How far can we go with NanoBanana 2? We will practice resolution, fonts, colors, and more, one by one. Using a stepping-stone reference method, we will create actual content ranging from logos to banners and videos.

[Sustainable AX, and How to Collaborate with AI Colleagues] Have you encountered these limitations while trying to apply AI tools to your practical work? 🙋‍♂️: "Entering commands from scratch every time is actually more of a hassle." 👨‍💻: "I feel fatigued by one-off tasks that just involve repeated copying and pasting." 🤷‍♂️: "Since I don't have development knowledge, I don't even dare to try deeper task automation." What we need right now is not just 'how to use a tool' to learn functions. It is a perspective of nurturing your own AI colleague that understands the context of your work and becomes smarter over time. We transparently reveal a practical AX (AI Transformation) guideline established by a non-developer planner through countless trials and errors in the field.

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.

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.

If you want to create your own AI service without coding, this course is the perfect solution. Using the Dify no-code platform, you can create high-quality AI apps with just your ideas. Instead of complex theory, it's structured around hands-on projects, allowing both AI beginners and working professionals to develop practical skills.

Do you want to generate significant profits even in a bear market? Are you interested in automated trading but haven't been able to start because of coding, trading strategy design, exchange APIs, or server deployment? In this course, we guide even those new to Python to turn trading ideas into concrete strategies using ChatGPT and Codex, and implement them into backtesting and live automated trading code. We will practice Binance Futures automated trading using Python and CCXT, technical indicator strategies, DCA (dollar-cost averaging) backtesting, and Upbit API-based Kimchi Premium strategies. Afterward, we will apply Telegram notifications and order safety features, manage the completed bot with GitHub, and deploy it to a cloud server. Rather than leaving all investment decisions to the LLM, humans will determine the strategies and risk management criteria, while using the LLM as an assistant to help with strategy design, coding, and troubleshooting. After completing the course, you won't just be running the provided code—you will be able to create, verify, modify, and deploy your own unique strategies!

We will create various design outputs such as logos, product photos, mockups, banners, wedding snaps, invitations, POPs, menus, and product detail pages using ChatGPT Images 2.0. This is a practice-oriented course where you will learn how to write prompts to achieve your desired results and master template structures that can be used repeatedly.

[Source Code Provided] No coding knowledge required. Build it instantly in your browser using Google's latest tool (IDX) without any installation! Stop studying coding syntax! This is an ultra-fast, hands-on course where you'll build an RAG chatbot that perfectly understands internal company documents through AI conversation (Vibe Coding) in the Google IDX environment and deploy it live to the web.

In this lecture, we will explore the practical structure of how AI agents open real browsers to research and make decisions on the web using the built-in Browser MCP in Antigravity. Rather than just simple usage, we focus on understanding the execution flow from Agent → Tool → Browser and the MCP-based expansion architecture. In 30 minutes, we will move beyond the concept of browser automation and structurally organize "how AI uses the web as a tool." https://antigravity.google/download

From a developer who only used ChatGPT to a developer who handles AI agents. Learn practical ways to maximize coding productivity using Codex's Rules, Hooks, Skills, and MCP.

Turn your dormant "notebook" into an "AI Workspace" that works on its own! Connect Notion DB with agents to complete a system where repetitive tasks run themselves.

AI Tech DNA Series: Core Principles of Computer Basics from an AI Perspective is an introductory course designed to help you easily understand the fundamental computer concepts necessary for the AI era. By explaining basic principles such as CPU, memory, storage, networks, and data structures through real-life analogies and an AI-focused lens, it builds the solid foundational strength needed to advance into Python, AI, security, and the cloud.

We explore how to create an AI judge by combining knowledge graphs, reasoning systems, and various agent frameworks such as CrewAI, LangChain, and AutoGen, which enable the implementation of LLM-based multi-agent technology.

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.

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.

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."

■ Course Overview Are you interested in 3D video production but feel it might be too difficult? Or do you want to create unique videos that stand out from the crowd? With CreateStudio3, you can easily create professional 3D animated videos without any coding. In this course, you will learn the basics of video production through step-by-step explanations that are easy for even beginners to understand. Through this course, you will master the basic operations of CreateStudio3 in a short period and become able to create your own original 3D animated videos. ■ What You Will Achieve By taking this course, you will be able to: - Experience and internalize the basic operations of CreateStudio3 through hands-on practice. - Create unique 3D animated videos. - Create highly flexible scenes by moving viewpoints and angles. - Achieve marketing and promotional results using video. - Acquire simple video production skills. ■ Who This Course Is For - Those who tried complex software like Blender but gave up. - Those who feel paid tools like VYOND are too expensive. - Those who want to create videos easily without coding. - Those who want to share unique 3D videos on social media or YouTube. - Those who want to publish videos without showing their face. ■ Required Tools and Costs - A computer *The instructor uses a Mac, but Windows is also supported. - Purchase of CreateStudio3 is required. (Detailed instructions on how to purchase it at a great price will be explained within the course.) Take this opportunity to master CreateStudio and start creating professional 3D animated videos. This experience is sure to be a major plus for your career. The main contents of this course are as follows: - Basic operations of CreateStudio3 - Basic techniques for 3D animation - Creating character animations - How to use effects - Scene settings and video updates - How to move the viewpoint - Video exporting and specific output methods I look forward to seeing you in the course. Hiroshi Wada