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About Deep Learning Assignment2 Solutions PDF
The document discusses the cross entropy loss function and its derivatives with respect to weights (w) and biases (b) of a neural network model. It then provides solutions to multiple choice questions related to cross entropy loss, neural network architectures, logistic functions, and gradient descent. Specifically, it derives the partial derivatives of cross entropy loss as ∇w = -y*(1 - f(x))*x and ∇b = -y*(1 - f(x)). It explains the weights needed for a multi-layer perceptron network to behave as an XOR function. It states that increasing w increases the slope of the logistic function, while increasing b shifts the center point left. Finally, it provides a link to
Detailed Information
| Author: | ['manas Singhal'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 16 |
| Language: | English |
| Format: | |
| Price: | FREE |
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