Consider the very small neural network below, with 2 inputs and no biases, and with theactivation function being the identity function, f(z)=z. The input (1, -1) is presented to thenetwork, as illustrated. The layer 2 activations have been calculated, and are shown. Now, whatare the layer 3 activations, i.e., the outputs?inputs(1)a₁ = 1x₁=1X₂=-1(1, -2)(1)az = -1(-2, 1)(1, 2)W12(2, 1)‚(1)_W11(1)W22W214a₁(2)az= 2W21(2)W12= 1(2)Wil=-1= 1W22= -1-outputsa₁ =?(3)az

Neural network: Neural network is also called as Artificial neural network or Simulated neural…

Answered: Consider the very small neural network…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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We consider a neural network with 100 neurons in the input layer and 10 neurons in the hidden layer and these two layers are fully connected between, how many weight parameters do we have between these two layers?
We are training a fully connected network with two hidden layers to predict housing prices. Inputs are 100-dimensional, and have several features such as the number of square feet, the median family income, etc. The first hidden layer has 1000 activations. The second hidden layer has 10 activations. The output is a scalar representing the house price. Assuming a vanilla network (e.g. no layer normalization, no learnable activation function parameters, etc.), how many parameters does this network have?
Let x, y, z be positive integers (x, y, z> 0) represented in unary. Assume that x > z. Design a Turing Machine (TM) that computes the function f (x, y, z) = x+y=z. More specifically, go w (x) Ow (y) Ow (z) +* qƒw (x+y=z) 0 Draw the transition graph of the TM. Make sure you clearly indicate the initial and final states. Do not draw the block diagram! Here is an example of a computation performed by the TM: if x = 5, y = 2, z = 4 then qo1111101101111 -* qƒ1110
PLZ answer 2.3.13
Suppose we are fitting a neural network with three hidden layers to a training set. It is found that the cross validation error Jcv(0) is much larger than the training error Jtrain (0). Should we increase the number of hidden layers?
Trace the Perceptron algorithm for the following input. Suppose the actual labels are with respect to a line passing through the origin with normal vector w* = (-1, 1); for example, the true label of (1,0) is '-1' and that of (0,1) is '+1'. The points are ₁ = (0, -1), x2 = (-1,0), x3 = (2,0), x4 = (√√2,1), x5 = (-3,-2). You need to follow the same assumptions as in the example in Slide 6 of Module 10. Show your work and specify the number of mistakes by the algorithm. x5: (-3,-2) 24=(√2,1) 22: (-1,0) xy: (2,0) (0,-1)
1. A neuron with 4 inputs has the weight vector w = [1; 2; 3; 4] and a bias, 0 = 0 (zero). The activation function given is f(net) = 2 * net. If the input vector is x = [4; 8; 5; 6], what will the output of the neuron be? 2. Assume that you want to build a solution by using the following network. The input for x 1 = 3 and x 2 = 2.5. The weight for the first layer is given which is 0.5 and second layer is 0.3. Calculate the possible output by using the summation function.
Justify your answer. 1.9 A neuron j receives inputs from four other neurons whose activity levels are 10, –20, 4, and -2. The respective synaptic weights of neuron j are 0.8,0.2, -1.0, and -0.9. Calculate the output of neuron j for the following two situations: (a) The neuron is linear. (b) The neuron is represented by a McCulloch-Pitts model. Assume that the bias applied to the neuron is zero.
There are many sensors on a wind turbine, based on each sensor's reading many alerts are generated as (start, end). For easier monitoring, our customer is asking to raise alerts on the turbine level instead of on each sensor. If any sensor is having an alert, there should be an alert for the entire wind turbine. Suppose the alerts created with two sensors readings are [(0,3),(5,10),(1,4),(10,15)], then the turbine level alert should be [(0,4),(5,15)]. Return the alerts sorted by the start timestamp. If there is no alert from the sensor level, return an empty array []. Code needs to be in Python?
Q3. The weights, activation function, and set of input values for a Neural Network are given below: Weights: wl = 2, w2 = -3, w3 = 4, w4 = -2, w5 = 5 1 when p > 0 O when p = 0 -1 when p <0 Set of input values (Set S1, S2, S3 and input values x1, x2, x3, x4, x5): Activation function: f (p): where p is weighted sum of inputs S1 S2 S3 x1 2 4 x2 3 2 3 x3 3 6 -2 x4 -4 -3 3 X5 -3 -2 3 What is the final output value for each set of input values?
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

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