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You use AI every day, but can you actually explain the difference between Machine Learning, Deep Learning, and an LLM? If you want to build with these tools or just stop nodding along in meetings—you need a map. In this video, I break down the entire landscape of Artificial Intelligence from the ground up. We’ll cover how models learn, why neural networks are "deep," the real difference between ChatGPT, Claude, and Gemini, and where the industry is heading next (Reasoning Models, AI Agents, and Open Source). This is Part 1 of a series where we go one level deeper into System Design and Tech Culture. Subscribe so you don't miss it! 👇 ⏳ TIMESTAMPS (Обязательно добавь их — это создает Chapters на плеере YouTube): 00:00 - The Problem with "AI" 00:50 - What is Artificial Intelligence & Machine Learning? 01:50 - How Models Learn (Supervised, Unsupervised, Reinforcement) 02:55 - Deep Learning & Neural Networks Explained 03:45 - Semi-Supervised Learning 04:10 - Discriminative vs Generative Models 04:55 - How Diffusion Models Work (Midjourney) 05:20 - Large Language Models (LLMs) & Transformers 06:00 - ChatGPT vs Claude vs Gemini 06:25 - The Future: Reasoning Models (System 1 vs System 2) 6:50 - AI Agents (The Action Loop) 07:05 - Open Source AI (Ollama vs Cloud) 7:18 - The Full AI Map Follow me on X (Twitter): https://x.com/lidiaintech Subscribe for more Tech & System Design breakdowns!
Answer each question based only on what was covered in the video lesson. No outside knowledge is needed — every answer can be found in the video.
Answer each question using what you learned in the video. Questions progress from straightforward recall to applying and distinguishing concepts — work through them in order.
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