Difference between revisions of "Backpropagation"
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| − | + | 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. [http://developers.google.com/machine-learning/glossary/ Machine Learning Glossary | Google] | |
Revision as of 06:45, 25 September 2020
Youtube search... ...Google search
- 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
