Domino Data Lab

APRIL 3, 2019

Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for Given those two, plus SQL gaining eminence as a database strategy, a decidedly relational picture coalesced throughout the decade. Andrew Ng later described this strategy as the “Virtuous Cycle of AI” – a.k.a.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84

The Unofficial Google Data Science Blog

APRIL 17, 2017

First, the system may not be understood, and even if it was understood it may be extremely difficult to measure the relationships that are assumed to govern its behavior. In the first plot, the raw weekly actuals (in red) are adjusted for a level change in September 2011 and an anomalous spike near October 2012.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Taking measurements at parameter settings further from control parameter settings leads to a lower variance estimate of the slope of the line relating the metric to the parameter.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

The Unofficial Google Data Science Blog

JUNE 30, 2016

A naïve comparison of the exposed and unexposed groups would produce an overly optimistic measurement of the effect of the ad, since the exposed group has a higher baseline likelihood of purchasing a pickup truck. 2012): 25-46. This is often referred to as the positivity assumption. Political Analysis 20.1 2014): 243-263.

Statistics 44

Statistics Optimization Modeling Experimentation 44

The Unofficial Google Data Science Blog

AUGUST 27, 2019

The measurement may be biased if our samples are generated from a procedure that samples without replacement, such as reservoir sampling , especially if some items have disproportionate weight, i.e., $p(v_i) cdot n$ is large. There are many strategies we can use to estimate this quantity, and we will discuss each option in detail.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

Domino Data Lab

AUGUST 8, 2019

Their approach is to bombard “organoid” mini brains living in vats with potential cancer meds, to measure the meds’ relative effects. The probabilistic nature changes the risks and process required. We face problems—crises—regarding risks involved with data and machine learning in production. Or something. what Chris describes.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Insight

DECEMBER 30, 2019

LeNet-5: A classic CNN architecture Since LeNet-5, there have been many different CNN architectures developed over the years, such as AlexNet (2012), GoogLeNet (2014), VGGNet (2014), and ResNet (2015). a circuit) and you measure the output signal, f(t) , given an exceedingly narrow input pulse (approximated as a Dirac delta function).

Visualization 72

Visualization Modeling Machine Learning IT 72

Sisense

JULY 23, 2021

Not only does it support the successful planning and delivery of each edition of the Games, but it also helps each successive OCOG to develop its own vision, to understand how a host city and its citizens can benefit from the long-lasting impact and legacy of the Games, and to manage the opportunities and risks created.

Unstructured Data 105

Unstructured Data Internet of Things Data-driven Data Processing 105

datapine

MAY 17, 2021

Businesses can benefit from improved data driven decision making as well as enhanced business processes and models and share insights across departments more fluently while propelling intelligent business strategies. The final aim of the data cleaning stage is to avoid the risks of working with misleading data that can damage your business.

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Of course, exploratory analysis of big unintentional data puts us squarely at risk for these types of mistakes. But this does not mean that the slice will continue to exhibit an extreme value on this measurement in the future. Controlling the Type I error necessarily comes at the expense of increasing the risk of a Type II error.

Experimentation 61

Experimentation Testing Statistics Metrics 61