Machine Learning Archives

Machine Learning

Watch: Kubeflow and Beyond: Automation of Model Training, Deployment and Testing

Machine Learning Modeling Pythonposted by ODSC Team July 12, 2019

Very often a workflow of training models and delivering them to the production environment contains loads of manual work. Those could be either building a Docker image and deploying it to the Kubernetes cluster or packing the model to the Python package and installing it to... Read more

OS for AI: How Serverless Computing Enables the Next Gen of ML

Business + Management Conferences Machine Learning Conferences West 2018posted by Daniel Gutierrez, ODSC July 11, 2019

Jon Peck is a Full Spectrum Developer & Advocate for Algorithmia, an open marketplace for algorithms. At ODSC West 2018, he delivered a talk “OS for AI” which discussed how serverless computing enables the next generation of machine learning. The slides for Peck’s presentation can be... Read more

Make Sense of the Universe with Rapids.AI

Machine Learning Modeling Kaggle Machine Learningposted by RAPIDS July 9, 2019

Classification of astronomical sources in the night sky is important for understanding the universe. It helps us understand the properties of what makes up celestial systems from our solar system to the most distant galaxy and everything in between. The Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC) wanted... Read more

Watch: A Manager’s Guide to Starting a Computer Vision Program

Business + Management Conferences Machine Learning Modeling Accelerate AI Computer Vision Machine Learningposted by ODSC Team July 9, 2019

This talk by Ali Vanderveld provides you with a guide for data science leaders and managers who are thinking about starting a computer vision program. It will help you think through a set of sequential questions that you’re likely to encounter along the way: [Related Article:... Read more

Image Augmentation for Convolutional Neural Networks

Machine Learning Modeling Python Convolutional Neural Networks Machine Learning Pythonposted by Nathaniel Jermain June 26, 2019

Limited data is a major obstacle in applying deep learning models like convolutional neural networks. Often, imbalanced classes can be an additional hindrance; while there may be sufficient data for some classes, equally important, but undersampled classes will suffer from poor class-specific accuracy. This phenomenon is... Read more

Optimizing Hyperparameters for Random Forest Algorithms in scikit-learn

Machine Learning Modeling Optimizing Hyperparametersposted by Nathaniel Jermain June 25, 2019

Optimizing hyperparameters for machine learning models is a key step in making accurate predictions. Hyperparameters define characteristics of the model that can impact model accuracy and computational efficiency. They are typically set prior to fitting the model to the data. In contrast, parameters are values estimated... Read more

Transforming Skewed Data for Machine Learning

Machine Learning Modeling Skewed Dataposted by Nathaniel Jermain June 24, 2019

Skewed data is common in data science; skew is the degree of distortion from a normal distribution. For example, below is a plot of the house prices from Kaggle’s House Price Competition that is right skewed, meaning there are a minority of very large values. Why... Read more

Essential Machine Learning with Linear Models in RAPIDS: Part 1 of a Series

Machine Learning Modelingposted by RAPIDS June 20, 2019

This blog is the first in a series about regression analysis in RAPIDS, an open GPU data science platform. There are many varieties of regression techniques, and we’re working to include them all in RAPIDS. In this blog edition, I use Ordinary Least Squares (OLS) and... Read more

Using RAPIDS with PyTorch

Deep Learning Machine Learning Modeling Tools & Languages Deep Learning Machine Learning rapidsposted by RAPIDS June 19, 2019

In this post we take a look at how to use cuDF, the RAPIDS dataframe library, to do some of the preprocessing steps required to get the mortgage data in a format that PyTorch can process so that we can explore the performance of deep learning on... Read more

The Empirical Derivation of the Bayesian Formula

Guest contributor Machine Learning Modeling Statistics bayseian Machine Learningposted by Jannes Klaas June 18, 2019

Deep learning has been made practical through modern computing power, but it is not the only technique benefiting from this large increase in power. Bayesian inference is up and coming technique whose recent progress is powered by the increase in computing power. We can explain the... Read more