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?

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?

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

See similar textbooks

Similar questions

Write a Java program to simulate the behaviour of a model-based agent for a vacuum cleaner environment based on the following conditions: The vacuum cleaner can move to one of 4 squares: A, B, C, or D as shown in Table 1. Table 1: vacuum cleaner environment A B C D The vacuum cleaner checks the status of all squares and takes action based on the following order: If all squares are clean, the vacuum cleaner stays in its current location. If the current location is not clean, the vacuum cleaner stays in its current location to clean it up. The vacuum cleaner can only move horizontally or vertically (cannot move diagonally). The vacuum cleaner moves only one square at a time. Horizontal moves have the highest priority over vertical moves. The vacuum cleaner moves to another square only when it needs to be cleaned up. If a diagonal square needs to be cleaned up, the vacuum cleaner moves to its neighbour vertical square first. The vacuum cleaner action is…
Let us take an example of a neural network simulating an XNOR function. So if u 0 %3D +1 0.5 -0.5 a, -1 a X, XNOR X2 1.5 X2 You can see that the last neuron takes input from two neurons before it. Suppose X1 is 0 and X2 is 1, what will be the output for the below neural network? 1 0.55 1.5
You are given the CFG S -> SS | (S) | [S] | a Transform it into a PDA. Before submitting, you may try the simulation. It works now.
Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing set 1. Required Model (100 pts): Implement and train a logistic regression as your classification model. • You have to use Sklearn deep learning library. • You may want to refer to this tutorial: https://bit.ly/37anOxi
A publisher plans to boost the sales of its most popular magazine by sending out promotional mails. We refer to a customer as a responder if he/she subscribes to the magazine for the next year after receiving a promotional mail. Otherwise the customer is referred to as a non-responder. Denote responder by C1 and non-responder by C2. The publisher has built a model to classify each customer as either a responder or a non-responder. In practice only 1% of the customers are responders, and the remaining 99% are non-responders. In order to build an unbiased model, the publisher employed the oversampling method in creating the training set and the validation set, such that both datasets contain 50% responders and 50% non-responders. The validation confusion matrix is given below. Actual Class C1 C2 Predicted Class C1 645 112 C2 255 788 Round your answers to 3 digits after the decimal point. The oversampling factor of C1 is . The oversampling factor of…
We can implement requests to the waiter as either a queue of requests or as a periodic retry of a request. With a queue, requests are handled in the order they are received. Th e problem with using the queue is that we may not always be able to service the philosopher whose request is at the head of the queue (due to the unavailability of resources). Describe a scenario with 5 philosophers where a queue is provided, but service is not granted even though there are forks available for another philosopher (whose request is deeper in the queue) to eat.If we implement requests to the waiter by periodically repeating our request until the resources become available, will this solve the problem described in the above Exercise? Explain.
Define 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
All businesses want to keep their customers happy. To do that, many companies assign a certain amount of time to each customer to optimize the business. The alternative would be to hire more employees, which would lead to higher costs. This problem requires you to simulate how authorities optimize the number of cashiers at the entrance of a toll bridge to make sure drivers are satisfied. Make the following assumptions: There is one cashier per line. The line works as a queue with no cars cutting the line or leaving it. One car arrives at the entrance every 10 seconds. It takes 90 seconds (1.5 minutes) to process the payment, starting from the moment a cashier is available. Assume there is no pause between cars. Written in java, Your Tasks: Design class CarInLine, with the following specifications: The class has two instance variables: arrivalTime and DepartureTime, stored as integers. Define a constructor that accepts an integer as an argument representing the arrival time, in…
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?
Consider an airport management system. Each airport has a unique id (e.g. Montreal:YUL). Let us introduce the types AIRPORT and CITY . We also introduce variable airports that contains associations between airport id’s and their corresponding host cities. A possible state of the system is shown below:airports ={YUL → Montreal,LCY → London_UKLHR → London_UK,MIL → Milan,SFO → San_Francisco,SDQ → Santo_Domingo}1. (PROGRAMMING) Define global variable airports in Common LISP and populate it with the above data. Demonstrate that the variable indeed contains the ordered pairs as shown above.2. Provide a declaration of variable monitored that holds all airport id’s.3. What kind of variable is airports? Provide a formal definition together with any and all applicable properties.4. Describe what data structure you would deploy to model variable airports. Note that you may not use a Dictionary. Should this be an ordered or an unordered structure? Discuss.5. Provide a formal specification of the…
3. Suppose a deep neural network N is used for the image quality assessment problem. The input of the problem is a pair of images 1₁ and 12. The output is a binary label L. L = 0 means that quality of 1₁ is higher than l₂ and L = 1 means that quality of l2 is higher than 1₁.The network design is as follows: 1₁ and 1₂ are separately passed through a sub-network N₁ (which has a few convolutional and fully connected layers) to get feature vectors f1 and f2. f1 and f2 would be concatenated and passed through another sub-network N₂ which has a few fully connected layers with ReLU activations. This sub-network is where the two images would be processed jointly. Finally, a sigmoid function would be used to predict the binary label L. Given a random image pair (A,B), the network says quality of A is higher than B. Can you guarantee that if you give (B,A) to the network, it will still say that quality of A is higher than B? If your answer is YES: Prove it. If your answer is NO: Explain what is…
Question 1 We designed a MLNN in Python. How many parameters are in the first hidden layer of this NN? Remember to count for bias. model_a=Sequential() model_a.add(Dense(input_dim=4, units=300, activation='tanh')) model_a.add(Dense(units=10, activation="tanh')) model_a.add(Dense(units=len(classes), activation='softmax')) model_a.summary() Question 2 In the previous question, what is the number of parameters in the second hidden layer?