Please can you answer the question How does the test loss change as learning progresses ? → C A Not secure playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=gauss®Dataset=reg-plane&learning Rate=0.03®ulari...3DATAWhich dataset doyou want to use?DARatio of training totest data: 50%Noise: 0Batch size: 10REGENERATE--▶IFEATURESWhich propertiesdo you want tofeed in?Epoch000,022X₂X₂X₁²X₂²X,X₂sin(X₁)Q Searchsin(X₂)Learning rate0.03Y+ --ActivationTanhYRegularizationNone0 HIDDEN LAYERS▾Regularization rate0OUTPUTTest loss 0.002Training loss 0.0015 4 3 2Y3 -2 -1Colors showsdata, neuron andweight values.-1Problem typeClassification*0532-0-1-2-60 1 2 3 4 5 6 12.3Table 1: Neural Network PlaygroundDataStructure****Q Search2 hiddenlayers:- 4 neuronsin 15-2 neuronsin 2nd1 hiddenlayer:-4 neuronsin 1stActivationFunctionTanhTanh89Training &Test Loss atlow epochs(pause atlow values)(29 epochs)Test loss0.218• A U ITrainingloss 0.165loss0.227(22 epochs)test loss0.228trainingB11 12 13qo'y+ 1214 15nTraining visualization atlow epochs16Training &Test Loss at>500 epochsTest loss0.001(540 epochs)Training loss0.001Calibri17(502 epochs)test loss0.019training loss0.00618عرض إدراج التنسيق أدوات مساعدةنص عادي19 20 212422YTrain & Test visualization at>500 epochs(select 'show test data')100%23ENG2425

The question is asking about how the test loss changes as learning progresses for two neural network…

1 2 3 4 51 Table 1: Neural Network Playground Data Structure 2 hidden layers: -4 neurons in 1st -2 neurons in 2nd 1 hidden layer: -4 neurons in 1st Activation Function Tanh Tanh 8 Training & Test Loss at low epochs (pause at low values) (29 epochs) Test loss 0.218 Training loss 0.165 (22 epochs) test loss 0.228 training loss 0.227 11 12 13 14 15 Training visualization at low epochs واع 16 Training & Test Loss at >500 epochs (540 epochs) Test loss 0.001 17 18 Training loss 0.001 (502 epochs) test loss 0.019 training loss 0.006 19 20 100% 6 21 22 23 24 Train & Test visualization at >500 epochs (select 'show test data')

1 2 3 4 51 Table 1: Neural Network Playground Data Structure 2 hidden layers: -4 neurons in 1st -2 neurons in 2nd 1 hidden layer: -4 neurons in 1st Activation Function Tanh Tanh 8 Training & Test Loss at low epochs (pause at low values) (29 epochs) Test loss 0.218 Training loss 0.165 (22 epochs) test loss 0.228 training loss 0.227 11 12 13 14 15 Training visualization at low epochs واع 16 Training & Test Loss at >500 epochs (540 epochs) Test loss 0.001 17 18 Training loss 0.001 (502 epochs) test loss 0.019 training loss 0.006 19 20 100% 6 21 22 23 24 Train & Test visualization at >500 epochs (select 'show test data')

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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#17
Massages exchange between managers is possible in: A) SNMPv2 only B) SNMPv1 & SNMPv2 C) SNMPv2 & SNMPv3 D) SNMPv1 only
The neural network given below takes two binary valued inputs x1,x2 ϵ{0, 1} and the activation function is the binary threshold function (h(x)=l if x>0;O otherwise). The OR logical functions does it compute Select one: True False
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
dont post existing one strict warning skip if you dont know strict warning
Artificial neural networks
dd = 17 uu = 1 ss %3D def pig(): global ss dd = uu + 4 ss = uu + dd ss += uu return ss def ant(): dd = uu + 13 dd = pig() return dd def bat(): global ss dd = uu + 4 %3D uu + dd Ss += uu ant() return ss def dog (): global uu uu = ss + 18 pig() return uu last_digit = (ant() + dog() + dd + uu print(last_digit) + ss) % 10 %3!
For the following identify test cases for all states, all events, all actions, and all transitions sequences. Empty pop/EmptyStackException push(x) pop [n == 1]/ return top(x) push(x) [n 1] / return top(x) push(x) [n == maxstack-1] pop / return top(x) push(x) / FullStackException Full
Which of the following statements about various neural network layers is true? (Multiple Answers Possible) A. A perceptron, or fully-connected layer, lacks the ability to capture class imbalance information in training data. B. A multi-layer perceptron can consider the interactions between multiple input fea- tures and an output class. C. A convolutional layer is best applied when the input to that layer contains local structure. D. A pooling layer can only be applied to 2-dimensional or higher input data. E. A vanilla RNN cell can be formulated as a fully-connected layer operation, half of whose inputs at each timestep from previous output and half from an input sequence.
MATLAB Project : Fuzzy Control of Air-conditioner • Use M a x -Min O p e r a tio n & S u g e n o’s Sim plifie d FI E • RoomTemperature ~ x, Outside Temperature ~ y Representative Values forx & y : [20 22.5 25 27.5 30 32.5 35] (oC) • Motor Speed forCompressor ~ z : [0 500 1000 1500 2000] (rpm) • Rule 1: IF (x is LOW [1.0 0.8 0.6 0.4 0.2 0 0]) AND (y is HIGH [00 0.2 0.4 0.6 0.8 1.0]) THEN (z is SLOW [0.6 1.0 0.6 0.2 0] ~ 500rpm) • Rule 2: IF (x is HIGH [0 0 0.2 0.4 0.6 0.8 1.0]) AND (y is LOW[1.0 0.8 0.6 0.4 0.2 0 0]) THEN (z is MEDIUM [0 0.5 1.0 0.5 0] ~1000 rpm) • Rule 3: IF (x is HIGH [0 0 0.2 0.4 0.6 0.8 1.0]) AND (y is HIGH[0 0 0.2 0.4 0.6 0.8 1.0]) THEN (z is FAST [0 0.2 0.6 1.0 0.6] ~1500 rpm) • Rule 4: IF (x is LOW [1.0 0.8 0.6 0.4 0.2 0 0]) AND (y is LOW[1.0 0.8 0.6 0.4 0.2 0 0]) THEN (z s STOP [1.0 0.6 0.2 0 0] ~ 0rpm) • Draw 3D plots for control surface z = f(x,y), andInterpolate x, y, z axes with 31, 31, 21 points, resp. • Outputs àM-Script file “your_program.m” and…
Subject: artificial intelligence Forward chaining and Backward chaining Suppose that the coding resulting from the acceptance of knowledge is as follows: A1 = body temperature >= 38°C A2 = cough A3 = cold A4 = persistent cough especially at night A5 = breath sounds P1 = common fever P2 = normal cough P3 = influenza / viral infection P4 = whooping cough P5 = respiratory tract infection The rules on the knowledge base include: R1 : IF A1 THEN P1 R2 : IF A2 THEN P2 R3 : IF P1 and (P2 or A3) THEN P3 R4 : IF P3 and A4 THEN P4 R5 : IF P3 and A5 THEN P5 The facts obtained from the user are that the fever, cough, and cough are more frequent at night (A1, A2, A4 → true). Explain the steps to get a conclusion if the inference technique used is: a. Forward chaining b. Backward chaining
AP7.10 A feedback system is shown in Figure AP7.10. Sketch the root locus as K varies when K> 0. Deter- mine a value for K that will provide a step response with an overshoot less than 5% and a settling time (with a 2% criterion) less than 2.5 seconds. 10 R(s) Y(s) (s + 2)(s + 5) K s + K FIGURE AP7.10 A nonunity feedback control system. +