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1. Express the loss function(L) in terms of x(i), yi), n, 00, 01. ƏL 2. Compute 900 მძი 3. Compute L 20₁ 4. Write update rules for 0 and 0₁

1. Express the loss function(L) in terms of x(i), yi), n, 00, 01. ƏL 2. Compute 900 მძი 3. Compute L 20₁ 4. Write update rules for 0 and 0₁

Advanced Engineering Mathematics
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Consider house rent prediction problem where you are supposed to predict price of a house based on just its area. Suppose you have n samples with their respective areas, x(¹), x(²),...,x(n), their true house rents y(¹), y(²),..., y(n). Let's say, you train a linear regressor that predicts ƒ (x(i)) : = 0 + 0₁x¹). The parameters and ₁ are scalars and are learned by minimiz- ing mean-squared-error loss through gradient descent with a learning rate a. Answer the following questions. 1. Express the loss function(L) in terms of x(i),y(i), n, 00, 0₁. 2. Compute L მძი ƏL 3. Compute 20₁ 4. Write update rules for 0 and 0₁
Transcribed Image Text:Consider house rent prediction problem where you are supposed to predict price of a house based on just its area. Suppose you have n samples with their respective areas, x(¹), x(²),...,x(n), their true house rents y(¹), y(²),..., y(n). Let's say, you train a linear regressor that predicts ƒ (x(i)) : = 0 + 0₁x¹). The parameters and ₁ are scalars and are learned by minimiz- ing mean-squared-error loss through gradient descent with a learning rate a. Answer the following questions. 1. Express the loss function(L) in terms of x(i),y(i), n, 00, 0₁. 2. Compute L მძი ƏL 3. Compute 20₁ 4. Write update rules for 0 and 0₁
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