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

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