Videos

Patrick Hayes originally gave this talk at ODSC West in 2018.

During Ai4 Finance, SigOpt CEO Scott Clark interviews SigOpt customer and CTO of Two Sigma, Matt Greenwood.

SigOpt CEO Scott Clark explains the value of multimetric optimization, a new capability embedded within the SigOpt Optimization Platform.

Join us as we explore how SigOpt is using AWS to optimize machine learning and AI pipelines.

Sneak away and catch up with Scott Clark, Co-Founder and CEO of Sigopt, a company whose software is focused on automatically tuning your model’s parameters through Bayesian optimization.

Learn how you can use SigOpt to optimize your trading models.

Learn how you can use SigOpt to optimize your AI and Machine Learning models.

Short tutorial on how to use SigOpt, AWS and TensorFlow to efficiently build a convolutional neural network for classifying digits in the SVHN dataset.

Common Problems In Hyperparameter Optimization: All large machine learning pipelines have tunable parameters, commonly referred to as hyperparameters.

During Ai4 Finance, SigOpt CEO Scott Clark interviews SigOpt customer and CTO of Two Sigma, Matt Greenwood.

In this video I’m going to show you how SigOpt can help you amplify your trading models by optimally tuning them using our black-box optimization platform.

Using Bayesian Optimization to Tune Machine Learning Models: In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive.

There are so many exciting and new ideas to make beautiful in machine learning.

In this video, we will walk through an example that uses SigOpt to tune a machine learning pipeline for a classification task.

In this talk we briefly introduce Bayesian Global Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive.

Short tutorial on how to use SigOpt, AWS and our scikit-learn wrapper to quickly optimize the hyperparameters, train and evaluate several classification models on a given dataset.

Bayesian Optimization methods used by SigOpt, coupled with the incredibly scalable deep learning architecture provided with the Nervana Cloud and neon, allow anyone to easily tune their models to quickly achieve higher accuracy.