Learn how gradient descent really works by building it step by step in Python. No libraries, no shortcuts—just pure math and code made simple.

University of Virginia School of Engineering and Applied Science professor Nikolaos Sidiropoulos has introduced a breakthrough in graph mining with the development of a new computational algorithm.

However, the gradient descent algorithms need to update variables one by one to calculate the loss function with each iteration, which leads to a large amount of computation and a long training time.

Find out why backpropagation and gradient descent are key to prediction in machine learning, then get started with training a simple neural network using gradient descent and Java code.

See Machine Learning for Beginners: An Introduction to Neural Networks for a good in-depth walkthrough with the math involved in gradient descent.