Backpropagation Explained: How AI Learns from Mistakes | Deep Learning Series скачать в хорошем качестве

Backpropagation Explained: How AI Learns from Mistakes | Deep Learning Series

Imagine turning in a massive group project and getting an F—immediately, the "Blame Game" begins. You need to know exactly who messed up so you can fix it next time. This is exactly what Backpropagation does for a neural network. It is the mathematical engine that travels backward from an error, pointing a finger at every single weight to say exactly how much it contributed to the failure. In this deep dive, we move beyond static architecture to learn how AI actually moves. We break down the two-phase rhythm of training: the Forward Pass (prediction) and the Backward Pass (learning). You will master the Chain Rule by visualizing computational graphs as circuit diagrams, learning how "Adder Gates" distribute blame and "Multiplier Gates" swap it. We also tackle the "Telephone Game" problem—the Vanishing Gradient—and explain why switching to ReLU saved Deep Learning from silence. Are you conducting a symphony of calculus? If the Chain Rule finally clicked for you today, hit that like button to send a positive gradient to the YouTube algorithm! Drop a comment below if you've ever had to "assign blame" in a real-life group project. Subscribe to Sumantra Codes for more deep dives into the math that powers GPT-4 and beyond. Chapters: 0:00 The Group Project Analogy (The Blame Game) 1:16 What is Backpropagation? 2:16 Forward Pass vs. Backward Pass: The Rhythm of AI 3:22 Computational Graphs as Circuit Diagrams 4:52 How Gates Work: Adders vs. Multipliers 6:48 The Chain Rule Masterclass 8:34 The Vanishing Gradient "Telephone Game" 9:45 Why ReLU Saved Deep Learning 10:16 PyTorch AutoGrad: Define-By-Run Flexibilty 11:21 Gradient Checkpointing: Training Massive LLMs 12:53 Summary: Conducting a Symphony of Calculus #DeepLearning #Backpropagation #ArtificialIntelligence #MachineLearning #PyTorch #DataScience #MathOfAI #SumantraCodes #Calculus