What are optimization algorithms?
Optimization algorithms help us to make our lives and work easier and better. They first identify ‘losses’ in our datasets – obstacles that stop us from improving, or that slow down an operation – and then they work out how to reduce them.
Several types of optimization algorithm exist. Among them are:
· Gradient descent
· Hill climbing
· Random search
None of these is one-size-fits-all. We have to know which algorithm to use (and yes, that means finding the optimal optimization algorithm!) to get the best results.
Optimization Algorithms in Python will help you to do just that.
What exactly do optimization algorithms help us do?
In the world of business, optimization algorithms will quickly become any machine learning engineer’s best friend. Such algorithms ensure that we are working in the most efficient way, which frees us up to do the important stuff.
We can use optimization algorithms to solve several real-world business problems.
Among others, they help us to:
· aid managers in choosing wise investments
· sync staff calendars
· help telecommunication companies design new optical networks
· assist couriers in planning goods deliveries
Why should I learn about optimization algorithms?
Learning to find the best solution based on evidence is an important life skill, and it is also critical to data science. Optimization algorithms weigh up the multiple aspects of a problem to find the best solution.
But how can we get our hands on them? How can we choose the right algorithm for the task at hand? And how do we get an optimization algorithm to identify what the best solution is for us?
In Artificial Intelligence: Optimization Algorithms in Python, we’ll explain how you can define all possible solutions to a problem, as well as their variables, restrictions and parameters. Then we’ll show you which algorithm will find the best pathway to the best solution.
Artificial Intelligence: Optimization Algorithms in Python helps you build and deploy optimization algorithms wherever you need to apply them.