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A case study on implementing gradient descent for machine learning applications

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Case Study: Machine Learning by Gradient Descent

Introduction

This case study looks at gradient descent, and the application of gradient descent to machine learning. We look at gradient descent from a programming, rather than mathematical, perspective. We'll start with a simple example that describes the problem we're trying to solve and how gradient descent can be used to solve it. We'll then look at three methods to compute gradients, the core of the problem:

  • numerical differentiation;
  • symbolic differentiation; and
  • automatic differentiation.

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A case study on implementing gradient descent for machine learning applications

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