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What is Gradient Descent?
A walk thru of the core concepts and resources to help solidify understanding
TL;DR: Gradient descent is a way to optimize model parameters to minimize error between predicted and actual results.
Why is Gradient Descent Import to Machine Learning?
Machine learning models are similar to writing a draft blog post. Your initial draft may be good, however, after iterating over the text to emphasize certain sections and correcting grammatical mistakes, your blog is ready to go in a much better state than when you stated. The true art in machine learning is selecting the correct features and adapting the parameters to improve a model for a specific task. Selecting the optimal value for parameters is accomplished by an optimization algorithm like gradient descent. Optimal parameters in a machine learning model will increase the accuracy of prediction on new data.
Important Terms in Gradient Descent (Jargon Alert)
Loss Function: Defines a good predictive model by quantifying how well an algorithm models given the data. An…
