The 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 examples

The 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 examples

C++ for Engineers and Scientists

4th Edition

ISBN:9781133187844

Author:Bronson, Gary J.

Publisher:Bronson, Gary J.

Chapter13: Structures

Section: Chapter Questions

Problem 4PP

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The language must be in python. Neural Network Units weights of [-1.2, -1.1, 3.3, -2.1] 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 sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examples
The language must be in python. Just do numbers 5-7 Neural Network Units Implement a single sigmoid neural network unit with weights of [-1.2, -1.1, 3.3, -2.1] Calculate the outputs for 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. Assuming that a sigmoid unit response >0.5 denotes a positive class and <0.5 is negative class, is example 1 positive or negative? is example 2 positive or negative? Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examples
The language must be in python. The code for parts 1-4 is below. Just do parts 5-7 Neural Network Units Implement a single sigmoid neural network unit with weights of [-1.2, -1.1, 3.3, -2.1] Calculate the outputs for 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. Assuming that a sigmoid unit response >0.5 denotes a positive class and <0.5 is negative class, is example 1 positive or negative? is example 2 positive or negative? Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examples import numpy as np import math def forward(x,w): t = np.dot(x.T,w) t = 1/(1+math.e**(-t))…
Project 1 dealt with single symbol Huffman Coding. Project 2 deals with Extended Huffman Codes. For instance, for a source emitting two symbols A and B, the second order extension involves coding messages AA, AB, BA and BB (22 in number). The third order extension involves messages such as AAA, AAB, etc. (23 in number). The probabilities of such strings are computed by multiplying the individual probabilities. For this project, use the Matlab code you have developed in Project 1 to perform third, fourth and fifth order extensions of a source message. 1. Choose an alphabet a set of at least six (6) symbols with assigned probabilities. 2. Compute the third, fourth and fifth order extension probabilities. 3. Using the built-in algorithm, derive the Huffman Code for each extension. 4. Compute the following quantities: (i) Average length of the codeword; (ii) The code efficiency; (iii) The Compression Ratio; (iv) Speed of computation.
Please answer the following question in depth with full detail. Consider the 8-puzzle that we discussed in class. Suppose we define a new heuristic function h3 which is the average of h1 and h2, and another heuristic function h4 which is the sum of h1 and h2. That is, for every state s ∈ S: h3(s) =h1(s) + h2(s) 2 h4(s) =h1(s) + h2(s) where h1 and h2 are defined as “the number of misplaced tiles”, and “the sum of the distances of the tiles from their goal positions”, respectively. Are h3 and h4 admissible? If admissible, compare their dominance with respect to h1 and h2, if not, provide a counterexample, i.e. a puzzle configuration where dominance does not hold.
Question5: Write a python program to train neural network to classify two clusters in a 2-dimensional space for implementing XOR, where the weight is -0.50 and x>-0.5. Also name the class by your name
Modeling the spread of a virus like COVID-19 using recursion. Let N = total population (assumed constant, disregarding deaths, births, immigration, and emigration). S n = number who are susceptible to the disease at time n (n is in weeks). I n = number who are infected (and contagious) at time n. R n = number who are recovered (and not contagiuous) at time n. The total population is divided between these three groups: N = S n + I n + R n There are several hidden assumptions here that may or may not apply to COVID-19, such as a recovered person is assumed to not be able to get the disease a second time, at least within the time window being examined. On week 0 (the start), you assume a certain small number of people have the infection (just to get things going). Everyone else is initially susceptible, and no one is recovered. There are two constants of interest: Let period = time period that it takes for an infected person to recover (recover meaning they become not infectious to…
Answer the given question with a proper explanation and step-by-step solution. You are asked to pick up a project on building highways to connect all cities in the country. The cost of building a highway between two cites i and j is c(i, j) > 0. If you were in charge from the beginning, this would have been a minimum spanning tree problem and could be solve easily with the algorithms covered in class. Since you pick it up halfway, however, some suboptimal choices have already been made by your predecessor. In other words, highways were already built between some pairs of cities. Design an algorithm to find a cost minimizing set of highways to built subject to the choices already made. Do not copy others.
Wireless sensor networks are a special kind of network that may transmit data. For data transmission between nodes and the base station, WSNs rely on sensor nodes. The computing resources and storage space of a sensor node are limited. In this thought experiment, we will consider a decomposable algorithm. To solve these subproblems at separate sensor nodes, would you prefer use divide and conquer or dynamic programming? Use succinct language.
Prolog Consider the (directed) network in the attached document. We could represent this network with the following Prolog statements: link(a,b). link(a,c). link(b,c). link(b,d). link(c,d). link(d,e). link(d,f). link(e,f). link(f,g). Now, given this network, we say that there is a "connection" from a node "X" to a node "Y" if we can get from "X" to "Y" via a series of links, for example, in this network, there is a connection from "a" to "d", and a connection from "c" to "f", etc. Formulate the appropriate Prolog rule "connection(X,Y)" which is true if (and only if) there is a "connection" from "X" to "Y" as described above --- note that this rule will be recursive. Test this rule out on the above network, to see if it is working correctly. Once it is working correctly, you will note that, e.g., the query "connection(a,e)." will give "true" multiple times. This means something, actually:
A wave is modeled by the wave function: y (x, t) = A sin [ 2π/0.1 m (x - 12 m/s*t)] y1 (t) = A sin (2πf1t) y2 (t) = A sin (2πf2t) Using any computer program, construct the wave dependency graph resultant y (t) from time t in the case when the frequencies of the two sound waves are many next to each other if the values are given: A = 1 m, f1 = 1000 Hz and f2 = 1050 Hz. Comment on the results from the graph and determine the value of the time when the waves are with the same phase and assemble constructively and the time when they are with phase of opposite and interfere destructively. Doing the corresponding numerical simulations show what happens with the increase of the difference between the frequencies of the two waves and vice versa.
Recall that histograms are used for constructing load-balanced range partitions. Suppose you have a histogram where values are between 1 and 100, and are partitioned into 10 ranges, 1–10, 11–20,...,91–100, with frequencies 15, 5, 20, 10, 10, 5, 5, 20, 5, and 5, respectively. Give a load-balanced range partitioning function to divide the values into 5 partitions. Write an algorithm for computing a balanced range partition with p partitions, given a histogram of frequency distributions containing n ranges.