Assume a 3-input perceptron plus bias (it outputs 1 if net > 0, else 0). Assume a learning rate c of 1 andinitial weights 0. The perceptron learning rule is: ∆wi = c(t – z) x i . Given the following training data set:1 0 0̶ ̶ > 00 1 1̶ ̶ > 11 0 1̶ ̶ > 11 1 0̶ ̶ > 01 1 1̶ ̶ > 00 0 1̶ ̶ > 0Please demonstrate the learning process for 1 epoch by filling the following table:Pattern Target Weight Vector Net Output ∆W1 0 0 1 00 1 1 1 1

Assume a perceptron:with 3 inputs (x1,x2,x3) plus a bias (x0) statically set to 1with weighted input= x0*w0+x1*w1+x2*w2+x3*w3that outputs 1 if weighted input > 0, else 0with initial weights are all set to 0with weight updating as follows: Wi j+1= Wi j+ a * (Target j- Output j) * X i and a learning rate a=1 How will the final weight vector look like when all data-items are processed? 1 0 1 1 0 0 -1 0 0 -1 0 0 1 0 1 0 None of the above

For the truth tables below, is it possible for a perceptron to learn the required output?Explain your reasoning.Input X 0 0 1 1Input Y 0 1 0 1Required Output 1 0 1 0Input X 0 0 1 1Input Y 0 1 0 1Required Output 1 0 0 1[6 Marks]

1 . Pertanyaan : Teknik memperbarui bobot dan bias dari output layer menuju input layer pada metode recurrent neural network disebut dengan…A. BackpropagationB. Feed forward propagationC. Hyperplane D. Perceptron E. Multi layer perceptron2 . Pertanyaan : Misalkan a=0, dan fungsi sigmoid adalah , nilai h'(a) adalah…A. 0.10B. 0.025C. 0.5D. 0.025E. 0.053 . Pertanyaan : Di bawah ini metode yang menggunakan konsep neural network, kecuali…A. Convolutional neural networkB. Support vector machineC. Deep neural networkD. Long short term memory (LSTM)E. Recurrent neural network4 . Pertanyaan : Misalkan diketahui    adalah parameter bias, dan  adalah bobot, sehingga nilai neuron pada hidden layer pertama adalah...A. 1B. 0.01C. 0.5D. 0.1E. 55 . Pertanyaan : Misalkan a=0, dan fungsi  nilai (a) adalah…A. 0.01B. 0.5C. 0.05D. 0 E. 16 . Pertanyaan : Alasan perlu dilakukannya stemming dalam analisis data teks adalah …A. Menambahkan kata dengan makna baru yang dapat menggantikan kata yang sebelumnya ada pada dokumenB. Mempercepat proses dari analisis yang dilakukanC. Kata dengan imbuhan yang berbeda memiliki kata dasar yang sama sehingga memiliki makna yang serupaD. Semua salahE. Untuk memperpendek setiap kata pada dokumen7 . Pertanyaan : Teknik memperbarui bobot dan bias dari output layer menuju input layer pada metode recurrent neural network disebut dengan…A. Feed forward propagationB. Multi layer perceptronC. Perceptron D. BackpropagationE. Hyperplane 8 . Pertanyaan : Tokenizing didefinisikan sebagai proses …A. Pengubahan teks menjadi angka-angkaB. Mengubah teks menjadi vectorC. Pemisahan teks menjadi potongan-potonganD. Pengambilan informasi penting dari teksE. Melakukan analisis terhadap informasi yang ada pada teks9 . Pertanyaan : Misalkan a=1, dan fungsi sigmoid adalah nilai h(a) adalah…(hasil dalam tiga angka desimal)A. 0.384B. 0.673C. 0.983D. 0.274E. 0.73110 . Pertanyaan :Bagaimana keuntungan dari desain gamifikasi pada platform mobile menciptakan pengalaman yang sesuai dengan mobilitas pengguna?A. Antarmuka yang sederhanaB. Responsivitas yang tinggiC. Pengalaman konsisten di berbagai perangkatD. Integrasi notifikasi mobileE. Pemberian poin atau reward yang sesuai dengan mobilitas pengguna

PerceptronPerceptron is Machine Learning algorithm for supervised learning of various binary classificationtasks. Further, Perceptron is also understood as an Artificial Neuron or neural network unit that helpsto detect certain input data computations in business intelligence.Perceptron model is also treated as one of the best and simplest types of Artificial Neural networks.However, it is a supervised learning algorithm of binary classifiers. Hence, we can consider it as asingle-layer neural network with four main parameters, i.e., input values, weights and Bias, net sum,and an activation function. Input Nodes or Input Layer:This is the primary component of Perceptron which accepts the initial data into the system for furtherprocessing. Each input node contains a real numerical value. Wight and Bias:Weight parameter represents the strength of the connection between units. This is another mostimportant parameter of Perceptron components. Weight is directly proportional to the strength of theassociated input neuron in deciding the output. Further, Bias can be considered as the line of interceptin a linear equation. Activation Function:These are the final and important components that help to determine whether the neuron will fire ornot. Activation Function can be considered primarily as a step function.Types of Activation functions: Sign function Step function, and Sigmoid functionThe data scientist uses the activation function to take a subjective decision based on various problemstatements and forms the desired outputs. Activation function may differ (e.g., Sign, Step, and Sigmoid)in perceptron models by checking whether the learning process is slow or has vanishing or explodinggradients

What is the main function of a perceptron in an artificial neural network?<br /> A. 1. To compute a weighted sum of its inputs and apply an activation function to determine the output. <br />B. 2. To reduce the dimensionality of the inputs based on an activation function and learning rate. <br />C. 3. To cluster input data into positive and negative categories, followed by a sigmoid function. <br />D. 4. To optimize the network parameters using gradient descent and backward propagation.