Stochastic Optimization Techniques

Stochastic optimization techniques are important in solving large-scale and complex problems in machine learning. Unlike deterministic approaches, which may struggle with high-dimensional data or become computationally expensive, stochastic methods offer strong alternatives that make use of randomness to find optimal solutions more efficiently.

In this chapter, learners will look into the principles and applications of stochastic optimization, gaining insights into algorithms particularly well-suited for large datasets and non-convex functions. Topics will include stochastic gradient descent (SGD) and its various enhancements, such as momentum, RMSProp, and Adam, each designed to improve convergence and stability.

By the end of this chapter, you will understand how to implement these techniques to optimize machine learning models effectively, appreciating their strengths and potential challenges.