Gradient Descent — Topic Summaries
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Gradient descent, how neural networks learn | Deep Learning Chapter 2
Gradient descent is the engine behind neural-network learning: it repeatedly nudges thousands of adjustable weights and biases to reduce a single...
Backpropagation calculus | Deep Learning Chapter 4
Backpropagation’s calculus boils down to one practical question: how much does the cost change when a single weight or bias nudges a network’s...
The Most Important Algorithm in Machine Learning
Backpropagation is the shared engine behind modern machine learning: it turns the goal of minimizing prediction error into a practical, efficient...
Lecture 1: Deep Learning Fundamentals (Full Stack Deep Learning - Spring 2021)
Deep learning fundamentals hinge on a simple but powerful idea: neural networks are flexible function approximators whose weights can be trained by...