[Solved] ECE472 Deep Learning Assignment 1

tldr: Perform linear regression of a noisy sinewave using a set of gaussian basis

functions with learned location and scale parameters. Model parameters are

learned with stochastic gradient descent. Use of automatic differentiation is

required. Hint: note your limits

Problem Statement Consider a set of scalars {x₁,x₂,,x_N} drawn from U(0,1) 012 and a corresponding set {y₁,y₂,,y_N} where:

(3)

^4. for all (x_i,y_i) pairs. Estimates for the parameters must be found using stochastic

gradient descent. A framework that supports automatic differentiation must be

used. Set N = 50,_noise = 0.1. Select M as appropriate. Produce two plots. First,

show the data-points, a noiseless sinewave, and the manifold produced by the

regression model. Second, show each of the M basis functions. Plots must be of

suitable visual quality. 4 2 0 2 4 4 2 0 2 4 x x

Figure 1: Example plots for models with equally spaced sigmoid and gaussian basis functions.