Gradient Descent Explained | The Math Behind How AI Learns | Deep Learning Series скачать в хорошем качестве

Gradient Descent Explained | The Math Behind How AI Learns | Deep Learning Series

Imagine being dropped onto a random mountain range in the dead of night, blindfolded, with no map or GPS. Your only way to survive is to find the absolute lowest point in the landscape before you freeze. This isn't just a survival scenario—it's exactly how Gradient Descent, the engine of all modern AI, navigates trillions of data dimensions to "learn" solutions for models like GPT-4 In this Deep Learning deep dive (Episode 4), we master the math behind the hiker. We break down the Golden Rule of Gradient Descent: always moving in the opposite direction of the gradient to minimize the loss function J(θ). You’ll discover the "Goldilocks Problem" of the Learning Rate (alpha)—why a step that’s too small bankrupts your GPU credits, while a step that’s too large leads to catastrophic divergence9. We also explore the "Himalayas" of non-convex landscapes, tackling local minima traps and comparing the three essential variants: Batch, Stochastic (SGD), and Mini-Batch Descent10. Join the Discussion: Have you ever seen your model's loss explode to NaN? You probably hit the "Huge Alpha" problem! Drop your debugging horror stories in the comments below. If this visual breakdown helped the math "click," please like, subscribe to SumantraCodes, and hit the bell for our next session on Backpropagation! Chapters: 0:00 The Terrifying Hiker Metaphor 1:03 Gradient Descent: The Driver of AI 1:53 The Mission: Minimizing J(θ) 2:40 Using Calculus to Find "Downhill" 3:52 Derivative vs. Gradient 4:24 The Step Size: Learning Rate (Alpha) 4:44 The Goldilocks Problem: Tiny vs. Huge Alpha 6:18 Convex vs. Non-Convex (The Himalayas) 7:18 The Trap: Local Minima & Saddle Points 8:00 Variant 1: Batch Gradient Descent 8:35 Variant 2: Stochastic Gradient Descent (SGD) 9:08 Variant 3: Mini-Batch Gradient Descent 9:53 Summary: Visualizing the Hiker #DeepLearning #MachineLearning #Gradient Descent #AI #DataScience #MathOfAI #SumantraCodes #NeuralNetworks