AutoML

Natural Language Processing

Ramping up your custom NLP tasks with Verseagility

On this episode of the AI Show, Timm Walz joins Seth to talk about how ramping up your custom NLP tasks with Verseagility  will benefit your data science workflow. He’ll demonstrate how to use the toolkit in combination with Azure Machine Learning.

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Databricks Machine Learning

Scaling AutoML-Driven Anomaly Detection with Luminaire

Zillow has built an orchestration framework around Luminaire, our open-source python library for hands-off time-series Anomaly Detection. Luminaire provides a suite of models and built-in AutoML capabilities which they process with Spark for distributed training and scoring of thousands of metrics. In this talk, learn the architecture of this framework and performance of the Luminaire […]

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Data

Dataiku End-to-End Demo

This demo uses a project that predicts flight delays to demonstrate connecting to data, preparing and enriching it, building machine learning models, and operationalizing your work entirely in Dataiku.

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Azure Machine Learning

Accelerate Your Data Science Projects with Azure AutoML

Here’s a great session from the recent Global Azure Data & AI Fest: Accelerate Your Data Science Projects with Azure Machine Learning’s AutoML Feature” delivered by Jon Tupitza.

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Databricks Machine Learning

Erin Ledell on AutoML

Databricks hosts this great show, called Data Brew and this episode they cover AutoML. Erin LeDell shares valuable insight on AutoML, what problems are best solved by it, its current limitations, and her thoughts on the future of AutoML. We also discuss founding and growing the Women in Machine Learning and Data Science (WiMLDS) non-profit.

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Databricks

ML Applications. Data Brew

For the second season of Databricks’ Data Brew, we will be focusing on machine learning, from research to production. We will interview folks in academia and industry to discuss topics such as data ethics, production-grade infrastructure for ML, hyperparameter tuning, AutoML, and many more. Good machine learning starts with high quality data. Irina Malkova shares […]

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AI Research

Directions in ML: Taking Advantage of Randomness in Expensive Optimization Problems

Optimization is at the heart of machine learning, and gradient computation is central to many optimization techniques. Stochastic optimization, in particular, has taken center stage as the principal method of fitting many models, from deep neural networks to variational Bayesian posterior approximations. Generally, one uses data subsampling to efficiently construct unbiased gradient estimators for stochastic […]

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Machine Learning

Intro to AutoML on Azure

Dr G shows us how to make use of the very handy Automated ML feature of Azure Machine Learning. 

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AI Deep Learning

Deep Learning New Frontiers

MIT Introduction to Deep Learning 6.S191: Lecture 6 with Ava Soleimany. Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!! Lecture Outline 0:00 – Introduction 0:58 – Course logistics 3:59 – Upcoming guest lectures 5:35 – Deep learning and expressivity […]

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