An Artificial Neural Network contains 2 neurons in the Input Layer (called neuron 0 and neuron 1), 2 neurons in the middle layer or hidden layer (called neuron 2 and neuron 3), and one neuron in the output layer (neuron 4). The depiction shows the structure of this Neural Network 5.9 46
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- Computer Science A way to avoid overfitting in Deep Neural Networks is to add an additional term R to the loss function L (for example L can be the cross entropy loss) as follows: L(w) + λR(w). (1) You know that one choice for R is the L2 norm, i.e. R(w) = ||w||2 2 . One friend of yours from the School of Maths told you however that there’s no need to use squares (i.e. powers of two) and that you can achieve the same effect by using absolute values, i.e. the L1 norm: R(w) = ||w||1. Would you agree with him? i.e. is the use of the L2 norm equivalent to using the L1 norm for regularization purposes? Justify your answerThe language must be in python. Neural Network Units two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the ReLU with respect to net input for both examplesDefine a fully connected network with two hidden layers with Nh1 and Nh2 features, respectively. The hidden layers should use relu activation and the final layer should not have any activation. a linear network with no hidden layer. We will use this network to study the benefit of depth, or equivalently using a non-linear network instead of a linear network. The final layer should not have any activation class NeuralNet(N.Module): # YOUR CODE HERE class LinearNet(N.Module): #YOUR CODE HERE
- The Harvard robotics club has organized a Robothon. n robots are placed alongthe edge of a circular area at the middle of the OAT(open air theatre). Each robot will move along arbitrary tracksinside the circle while leaving behind a heat signature along its trail. However, they have beenprogrammed not to cross their own trail or the trail of another robot, neither will they ever moveout of the circle. In case a pair of robots i and j meet at any point, they are removed from the sceneand the club will pay a reward sum of M[i, j] to the owners of these robots. Note that some robotscan keep moving infinitely without ever meeting another one. Given the reward matrix M whereM[i, j] = M[j, i], design a polynomial time algorithm that determines the maximum money theclub might potentially end up spending. For this particular problem, give a very brief justificationof the recurrence. Give pseudo-code for a dynamic program which solves the recurrence efficiently Youdo not need to prove…1. StrangeNEWS® just reported that suddenly a very VERY strange planet appeared out of nowhere! Three species, A, B and C are living on this planet. Any two different species can mate. If they do, two children will be born and they themselves will die. The planet will fail if there only one kind of specie left (therefore, no more mating can take place). We denote mating between two individuals from B and C species by a. Similarly, we define b and c. We want to draw an automaton for any given initial number of species on the planet that tells us whether a sequence of mating is possible and/or causes the planet to fail. For example, let's say there is only one individual of species A and one of species B. Then, the only way that a mating can happens is if these two individual mate with each other (mating of type c). We can draw a finite automaton for it as below: 110 As you can see, final state in this automoton is when the planet failes (not more mating possible or, in other words,…2: Back propagation: Consider the following neural network. Single-circled nodes denote variables (e.g. æ1 is an input variable, hy is an intermediate variable, y is an output variable), and double-circled nodes denote functions (e.g. Σ takes the sum of its inputs, and ♂ denotes the logistic function σ(x) = 1+e-x. In the network below, hi = l+e-T1w1T2W2 · 1 1 W1 = -1.7 Cloon Σ W2 = 0.1 1 h - 0.6 oblood Σ S2 W4 = - 1.8 W5 = ww 0.2 = 0.5 3
- 3. Assume a graph G = (V, E) represents a computer network where the vertices represent computers and the edges represent point-to-point network connections between the computers. Assume each edge (u, v) of G is associated with a real number f(u,v) between 0 and 1 which represents the probability that the corresponding network connection is faulty. Assume these probabilities are independent. a. Propose an algorithm that finds a path between two given vertices u and v with the smallest probability of being faulty (a path is faulty if at least one of its edges is faulty). b. Analyze the complexity of your algorithm.Correct answer will be upvoted else downvoted. Computer science. Assume you are living with two felines: An and B. There are n resting where the two felines normally rest. Your felines like to rest and furthermore like this load of spots, so they change snoozing spot every hour consistently: Feline A progressions its snoozing place all together: n,n−1,n−2,… ,3,2,1,n,n−1,… at the end of the day, at the main hour it's on the spot n and afterward goes in diminishing request consistently; Feline B changes its resting place all together: 1,2,3,… ,n−1,n,1,2,… all in all, at the primary hour it's on the spot 1 and afterward goes in expanding request consistently. The feline B is a lot more youthful, so they have a severe pecking order: An and B don't lie together. All in all, on the off chance that both cats'd prefer to go in spot x, the A has this spot and B moves to the following submit in its request (assuming x<n, to x+1, however on the off chance that x=n, to 1). Feline…Correct answer will be upvoted else downvoted. Computer science. in case there are two planes and a molecule is shot with rot age 3 (towards the right), the cycle is as per the following: (here, D(x) alludes to a solitary molecule with rot age x) the primary plane delivers a D(2) to the left and lets D(3) progress forward to the right; the subsequent plane delivers a D(2) to the left and lets D(3) progress forward to the right; the primary plane lets D(2) forge ahead to the left and creates a D(1) to the right; the subsequent plane lets D(1) progress forward to one side (D(1) can't create any duplicates). Altogether, the last multiset S of particles is {D(3),D(2),D(2),D(1)}. (See notes for visual clarification of this experiment.) Gaurang can't adapt up to the intricacy of the present circumstance when the number of planes is excessively huge. Help Gaurang find the size of the multiset S, given n and k. Since the size of the multiset can be extremely huge, you…
- TASK 3.1: System The following are other types of SYSTEM in Digital Signal Processing. Select 3 and write a short description of each. Describe the process of how each system process signals . DSP-Static Systems DSP - Dynamic Systems DSP Causal Systems DSP Non-Causal Systems DSP - Anti-Causal Systems DSP Non-Linear Systems DSP Time-Invariant Systems DSP Time-Variant Systems DSP Stable Systems DSP Unstable Systems ● .A produce dealer has a warehouse that stores a variety of fruits. He wants a machine capable of sorting the fruit according to the type. There is a conveyor belt on which the fruit is loaded. It is then passed through a set of sensors which measure 3 properties of the fruit: shape, texture, and weight. The sensor system is somehow rather primitive: • Shape sensor : -1 if the fruit is round and 1 if it is more elliptical• Texture sensor : -1 if the surface is smooth, 1 if it is rough• Weight sensor : -1 if the fruit is > 500g, 1 if is < 500g The sensor output will then be input to a Neural Networks based classifying system. As an AI Engineer you are supposed to design (draw the architecture and determine the optimal weight W and bias b) a simple neural network (could be a single perceptron) that can be used to recognize the fruit so that it can be directed to the correct storage bin. As a startup case, the simple network will only be used for two type of fruit i.e. banana and…A produce dealer has a warehouse that stores a variety of fruits. He wants a machine capable of sorting the fruit according to the type. There is a conveyor belt on which the fruit is loaded. It is then passed through a set of sensors which measure 3 properties of the fruit: shape, texture, and weight. The sensor system is somehow rather primitive: • Shape sensor :-1 if the fruit is round and 1 if it is more elliptical • Texture sensor :-1 if the surface is smooth, 1 if it is rough • Weight sensor :-1 if the fruit is > 500g, 1 if is < 500g The sensor output will then be input to a Neural Networks based classifying system. As an Al Engineer you are supposed to design (draw the architecture and determine the optimal weight W and bias b) a simple neural network (could be a single perceptron) that can be used to recognize the fruit so that it can be directed to the correct storage bin. As a startup case, the simple network will only be used for two type of fruit i.e. banana and apple.…