Overview Of Backpropagation In Neural Networks Training Ppt
This set of slides covers the concept of backpropagation along with its two types Static Backpropagation and Recurrent Backpropagation. Backpropagation helps calculate the gradient of a loss function with respect to all of the networks weights. They also list its advantages and disadvantages.
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Slide 1
This slide describes the concept of backpropagation along with its two types that are Static backpropagation and Recurrent backpropagation. Backpropagation is useful in calculating the gradient of a loss function with respect to all of the network's weights.
Instructor’s Notes:
- Static Backpropagation: The mapping of static input generates a static output in this type of backpropagation. It is used to address challenges like optical character recognition that requires static classification
- Recurrent Backpropagation: The Recurrent Propagation is directed forward or conducted till a specific set value, or threshold value is attained. The error is evaluated and propagated backward after reaching a particular value
Slide 2
This slide lists the advantages of Backpropagation. These are that it is simple & straightforward, adaptable & efficient, and it does not require any unique characteristics.
Slide 3
This slide lists the disadvantages of Backpropagation. These are that the data input determines the function of the entire network on a particular issue, networks are susceptible to noisy data, and a matrix-based technique is used.
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