Why in neural networks synchronous updates can lead to infinite loops and not allow to retrieve the final pattern, but asynchronous updates cant?

an input color RGB image with the height of 200 and the width of 200 has entered a Convolutional Neural Network that has the convolutional layer with 50 filters each with the size of 3x3. How many parameters does this convolutional layer (filter parameters and biases altogether) have?

Explain in mathematical terms the process of supervised learning in neural networks and the results achieved by using such learning.

Give an example of a situation when a recurrent neural network might be preferable to a non-recurrent one, and explain

With a diagram (no copy/paste), discuss a synapses for artificial neural networks

In pattern recognition, what exactly is the distinction between verification and identification?

Autoencoders are special type of neural networks trained to estimate identity functionTrueFalse

The following figure is a diagram of a small convolutional neural network that converts a 16x16 image into 4 output values (16x16 - 4x12x12 → 4x6x6 6x1). The network has the following layers/operations from input to output: convolution with 4 filters, max pooling, ReLu (after pooling), and finally a fully-connected layer (with no hidden layer). For this network we will not be using any bias/offset parameters. Please answer the following questions about this network. 16x16 Convolution 4 filters 5x5 Stride 1 4@12x12 max pooling 2x2 Stride 2 4@6x6 6x1 fully- connected