Convolutional Layers (CONV) in Deep Learning

Convolutional Layers (CONV) in Deep Learning
- Python and Machine Learning for Integrated Circuits -
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Python and Machine Learning for Integrated Circuits http://www.globalsino.com/ICs/

Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix

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Table 3828. Hidden layer in deep learning neural network.

Application example		Hidden Layer
Feedforward neural network for image classification	Details	Number of Nodes: Let's say we have one hidden layer with 128 nodes. The number of nodes in the hidden layer is a hyperparameter that can be adjusted based on the problem and complexity of the task. Activation Function: ReLU (Rectified Linear Unit) activation function is commonly used in hidden layers. So, ReLU activation is applied to the output of each node in this layer.
	Variables /nodes	128 nodes (ReLU activation)
	Included layers	Convolutional Layer (CONV) Pooling Layer (e.g., MaxPooling) Convolutional Layer (CONV) Pooling Layer Fully Connected Layer (FC)
	Energy usage	99% energy are used by Convolutional Layer (CONV) and Fully Connected Layer (FC)

* Feedforward neural network for image classification: this builds a neural network to classify handwritten digits from the MNIST dataset, where each image is a 28x28 pixel grayscale image of a handwritten digit (0 through 9).

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