Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. In this article, take a deep dive into data science and how Domino’s Enterprise MLOps platform allows you to scale data science in your business. In fact, deep learning was first described theoretically in 1943.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Domino Data Lab

JULY 17, 2021

Further, deep learning methods are built on the foundation of signal processing. The likelihood function measures the goodness of fit of a statistical model to a sample of data for given values of the unknown parameters. VoxCeleb1 dev test # of speakers 1,211 40 # of videos 21,819 677 # of utterances 148,642 4,874.

Modeling 64

Modeling Testing Machine Learning Measurement 64

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Domino Data Lab

APRIL 21, 2021

In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. Machine Learning-based detection – using statistical learning is another approach that is gaining popularity, mostly because it is less laborious. describe().

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

AUGUST 10, 2021

Some popular tool libraries and frameworks are: Scikit-Learn: used for machine learning and statistical modeling techniques including classification, regression, clustering and dimensionality reduction and predictive data analysis. PyTorch: used for deep learning models, like natural language processing and computer vision.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Smarten

APRIL 19, 2024

Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control. Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns. linear regression, trees) can be too rigid in their functional forms.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

DataRobot

NOVEMBER 9, 2021

Signal classification models are typically built using time series principles; traditionally used features that include central, windowed, lag, and lead statistics can do the job but sometimes there might be scenarios where we want to eke out more performance out of the data. Image courtesy towardsAI.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

Domino Data Lab

JUNE 23, 2019

Companies with successful ML projects are often companies that already have an experimental culture in place as well as analytics that enable them to learn from data. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics. It is similar to R&D. Transcript.

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Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MARCH 3, 2019

What metrics are used to evaluate success? I’m here mostly to provide McLuhan quotes and test the patience of our copy editors with hella Californian colloquialisms. The data types used in deep learning are interesting. The data types used in deep learning are interesting. Who builds their models?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

MAY 8, 2019

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. If your “performance” metrics are focused on predictive power, then you’ll probably end up with more complex models, and consequently less interpretable ones.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

O'Reilly on Data

JANUARY 25, 2021

Both SRE and DevOps emphasize similar practices: version control (62% growth for GitHub, and 48% for Git), testing (high usage, though no year-over-year growth), continuous deployment (down 20%), monitoring (up 9%), and observability (up 128%). It’s particularly difficult if testing includes issues like fairness and bias.

Machine Learning 363

Machine Learning Software Testing Technology 363