AI Fundamentals (Part 1): Machine Learning, Deep Learning & Generative AI скачать в хорошем качестве

AI Fundamentals (Part 1): Machine Learning, Deep Learning & Generative AI

In this video, we explore the mathematics behind Artificial Intelligence — from fundamentals to modern Large Language Models. We begin with the broad divisions of AI: Machine Learning Deep Learning Generative AI Then we break down the three core mathematical pillars that power AI systems: • Linear Algebra – How data (text, images, audio) is represented using vectors, matrices, and embeddings • Calculus – How models learn using gradients, backpropagation, and optimization • Probability & Statistics – How models make predictions and generate outputs We also explain: Regression, Classification, and Clustering Neural Networks (ANN, CNN, RNN) Transformers and how they power modern LLMs What parameters really mean (weights, floating points) Why Generative AI is fundamentally a probability model Temperature, Top-K, Top-P, Softmax explained Finally, we connect everything back to Linear Regression, the “Hello World” of AI, and understand how it scales into modern large models. This session is designed for: Students learning AI fundamentals Developers transitioning into AI/ML Anyone curious about how AI really works behind the scenes AI is not magic — it is mathematics, optimized at scale. If you’d like a deep-dive video on embeddings or any specific topic covered here, let me know in the comments. Timeline: 0:00 Introduction to AI Fundamentals 2:29 Broad Divisions of AI 3:14 Machine Learning (Regression, Classification, Clustering) 4:18 Deep Learning (ANN, CNN, RNN, Transformers) 5:32 Generative AI (LLMs, Multimodal Models, LAMs) 6:09 Mathematics in AI 6:28 Linear Algebra in AI (Embeddings, Matrices, Vectors) 8:40 Calculus in AI (Derivatives, Gradients, Backpropagation) 10:37 Probability & Statistics in AI (Softmax, Temperature, Distributions) 12:35 Part2 - Linear Regression – Hello World of AI