Have you ever been studying deep learning, only to lose sight of the bigger picture when model names and equations all appear at once? This course explains deep learning not as a collection of difficult formulas, but as the history of problem-solving that began with a single artificial neuron and progressed to ChatGPT. I have taught hundreds of hours of deep learning lectures to learners with diverse academic backgrounds and experiences at LG Electronics DX School. Through that process, I discovered a common difficulty: “I understand it while listening to the explanation, but when a new equation or model appears, I feel lost again.” So I improved the lectures by explaining what the problem was first, why existing methods failed, and what was changed to solve it—before covering the model’s structure or calculation methods. In this course, we follow a single visual metaphor—“drawing a line”—to connect the perceptron to CNNs, RNNs, Attention, Transformers, and large language models. Rather than calculating the equations directly, you will understand what problem each equation solves and learn to analyze new AI systems for yourself by asking the following question: “What problem was this model trying to solve, what did it change to do so, and what decisions did it leave to humans?” Ultimately, this course helps you develop the ability to distinguish between what should be entrusted to AI and what people must verify and take responsibility for, instead of blindly trusting or fearing AI.