Subject archive for "machine-learning," page 9

At The Climate Corporation, we aim to help farmers better understand their operations and make better decisions to increase their crop yields in a sustainable way. We’ve developed a model-driven software platform, called Climate FieldView™, that captures, visualizes, and analyzes a vast array of data for farmers and provides new insight and personalized recommendations to maximize crop yield. FieldView™ can incorporate grower-specific data, such as historical harvest data and operational data streaming in from special devices, including (our FieldView Drive) that are installed in tractors, combines, and other farming equipment. It incorporates public and third-party data sets, such as weather, soil, satellite, elevation data and proprietary data, such as genetic information of seed hybrids that we acquire from our parent company, Bayer.

Notebooks are increasingly crucial in the data scientist's toolbox. Although considered relatively new, their history traces back to systems like Mathematica and MATLAB. This form of interactive workflow was introduced to assist data scientists in documenting their work, facilitating reproducibility, and prompting collaboration with their team members. Recently there has been an influx of newcomers, and data scientists now have a wide range of implementations to choose from, such as Jupyter Notebook, Zeppelin, R Markdown, Spark Notebook, and Polynote.

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. If you are interested in additional insights, register for the upcoming Ray Summit.

This article provides concise insights into GANs to help data scientists and researchers assess whether to investigate GANs further. If you are interested in a tutorial as well as hands-on code examples within a Domino project, then consider attending the upcoming webinar, “Generative Adversarial Networks: A Distilled Tutorial”.