Calculus for Machine Learning: Intuition, Backprop, and The Chain Rule Explained скачать в хорошем качестве

Calculus for Machine Learning: Intuition, Backprop, and The Chain Rule Explained

Is Calculus actually necessary for Machine Learning, or is it just academic gatekeeping? In this video, we strip away the complex notation to reveal the honest truth about why math matters for your code. [Body] If you search online, you'll find two camps: those who say "just import TensorFlow" and those who say "derive it by hand." Both are wrong. In this video, we look under the hood of the "Black Box" of AI. We explore why Calculus isn't just about passing an exam—it is the literal engine that allows Neural Networks to learn. We cover the intuition behind the Loss Function, the Gradient, and the infamous Chain Rule (Backpropagation) without getting bogged down in proofs. If you want to move from "running code" to "architecting systems," this video is for you. [Key Topics Covered] The Myth: Why "Camp No-Math" and "Camp Math-Heavy" are both misleading. The Intuition: Visualizing the "Foggy Mountain" and Gradient Descent. The 3 Pillars: How the Loss Function, Gradient, and Backpropagation actually work together. The Reality Check: Do you need to solve integrals by hand at work? (Spoiler: No, but...) Actionable Steps: The 5 specific concepts you need to learn today. [Timestamps] 0:00 - The Two Camps of ML 1:45 - The "Foggy Mountain" Analogy 3:20 - Pillar 1: The Loss Function (Quantifying Failure) 5:10 - Pillar 2: The Gradient (Finding Direction) 7:30 - Pillar 3: Backpropagation (The Chain Rule) 9:45 - The Reality: Do I need to do this by hand? 11:20 - 5 Steps to Learn "Just Enough" Calculus 13:00 - Why Math prevent you from being replaced by AutoML [Recommended Book: "Mathematics for Machine Learning" [Tags] #MachineLearning #Calculus #DataScience #DeepLearning #Python #AI #MathForML #Backpropagation #GradientDescent