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A two-layer neural network is trained on a dataset of 10,000 photos to classify images as dogs or cats. The input layer contains 16x16x3=576 neurons to represent each pixel. With 10 hidden neurons, there are 576 input weights, 10 hidden weights, and 2 output weights and biases. The preferred activation for the output layer is sigmoid to perform binary classification of dog vs cat.

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A dog vs. cat dataset consisting of 10,000 photos is used to train a two-layer fully connected neural network that classifies photos as containing a dog or a cat. If each photo is 16 16 pixels with 3 color channels, then there are units in the input layer. Suppose that there are 10 hidden neurons. Then there are biases and weights in the neural network. Which activation function is preferred for the output layer?

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The impact of social media on mental health and well-being has been a topic o...The impact of social media on mental health and well-being has been a topic o...
The impact of social media on mental health and well-being has been a topic o...
 

A dog vs. cat dataset consisting of 10,000 photos is used to train a.pdf

  • 1. A dog vs. cat dataset consisting of 10,000 photos is used to train a two-layer fully connected neural network that classifies photos as containing a dog or a cat. If each photo is 16 16 pixels with 3 color channels, then there are units in the input layer. Suppose that there are 10 hidden neurons. Then there are biases and weights in the neural network. Which activation function is preferred for the output layer?