SlideShare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
SlideShare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
Successfully reported this slideshow.
Activate your 14 day free trial to unlock unlimited reading.
SigOpt helps your algorithmic traders and data scientists build better models faster. Learn how to integrate SigOpt into your modeling platform for quick ROI for your data science team.
SigOpt helps your algorithmic traders and data scientists build better models faster. Learn how to integrate SigOpt into your modeling platform for quick ROI for your data science team.
1.
*Accenture, Survey of AI Practices across 8,300 global enterprises
2.
Pro Con
Manual Search Leverages expertise Not scalable, inconsistent
Grid Search Simple to implement Not scalable, often infeasible
Random Search Scalable Inefficient
Evolutionary Algorithms Effective at architecture search Very resource intensive
Bayesian Optimization Efficient, effective Can be tough to parallelize
3.
Pro Con
Manual Search Leverages expertise Not scalable, inconsistent
Grid Search Simple to implement Not scalable, often infeasible
Random Search Scalable Inefficient
Evolutionary Algorithms Effective at architecture search Very resource intensive
Bayesian Optimization Efficient, effective Can be tough to parallelize
4.
Pro Con
Manual Search Leverages expertise Not scalable, inconsistent
Grid Search Simple to implement Not scalable, often infeasible
Random Search Scalable Inefficient
Evolutionary Algorithms Effective at architecture search Very resource intensive
Bayesian Optimization Efficient, effective Can be tough to parallelize