Machine Learning in Elixir

Wrapping Up

In this chapter, you framed machine learning as an optimization problem to understand how machine learning and optimization are related. Additionally, you learned about regularization and how the key difference between optimization and machine learning is machine learning’s emphasis on generalization. You applied optimization to a machine learning problem by implementing your own version of stochastic gradient descent. Finally, you learned a bit about black-box optimization and hyperparameter optimization in the context of machine learning.

With the foundations of machine learning and Nx covered, you’re ready to start solving real-world machine learning problems with Elixir and the Nx ecosystem. In the next chapter, you’ll jump straight ...