Backpropagation

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• Gradient Descent Optimization & Challenges
• Objective vs. Cost vs. Loss vs. Error Function
• Wikipedia
• Manifold Hypothesis
• How the backpropagation algorithm works
• Backpropagation Step by Step
• What is Backpropagation? | Daniel Nelson - Unite.ai
• Other Challenges in Artificial Intelligence
• A Beginner's Guide to Backpropagation in Neural Networks | Chris Nicholson - A.I. Wiki pathmind

The primary algorithm for performing gradient descent on neural networks. First, the output values of each node are calculated (and cached) in a forward pass. Then, the partial derivative of the error with respect to each parameter is calculated in a backward pass through the graph. Machine Learning Glossary | Google