Domino Data Lab

SEPTEMBER 9, 2019

This article provides a brief introduction to natural language using spaCy and related libraries in Python. Introduction. This article and paired Domino project provide a brief introduction to working with natural language (sometimes called “text analytics”) in Python using spaCy and related libraries.

Deep Learning 110

Deep Learning Machine Learning Visualization Data Science 110

CIO Business Intelligence

FEBRUARY 7, 2023

Black women in technology are burnt out and impatient with an IT industry slow to change. is made up of Black women. is made up of Black women. Some professionals said they had heard this phrase used directly about them, via a third-party, or through peers when sharing their own experiences. You haven’t got what it takes. I’ve heard that directly.”

Technology 143

Technology Advertising Software Management 143

Domino Data Lab

JULY 2, 2019

Paco Nathan ‘s latest article covers program synthesis, AutoPandas, model-driven data queries, and more. Introduction. In other words, using metadata about data science work to generate code. Then click the Synthesize button in AutoPandas to get back a snippet of code in Python. Program Synthesis.

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

Insight

AUGUST 16, 2019

The industry demand for Data Engineers is constantly on the rise, and with it more and more software engineers and recent graduates try to enter the field. The biggest hurdle for newcomers lies in understanding the Data Engineering landscape and getting hands-on experience with relevant frameworks. Want to Become a Data Engineer?

Big Data 51

Big Data Data Science Data Processing Machine Learning 51

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Introduction. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

MAY 8, 2019

Introduction. Admittedly, throughout large swaths of computer science, reductionism serves quite well. Admittedly, throughout large swaths of computer science, reductionism serves quite well. For example, common practices for collecting data to build training datasets tend to throw away valuable information along the way.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

AUGUST 8, 2019

Paco Nathan ‘s latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams. Introduction. In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

The Unofficial Google Data Science Blog

AUGUST 31, 2016

By DAVID ADAMS Since inception, this blog has defined “data science” as inference derived from data too big to fit on a single computer. Thus the ability to manipulate big data is essential to our notion of data science. Spark was developed at UC Berkeley to enable exploratory analysis and ML at scale.

Data Science 66

Data Science Sales Optimization Cost-Benefit 66

Insight

DECEMBER 30, 2019

Wikipedia In our last blog , we walked through a super fast introduction of “vanilla” flavored neural networks. Let’s use our ninja skills to figure out what CNNs are really doing. Lauren Holzbauer was an Insight Fellow in Summer 2018. We talked about how a vanilla neural net is made up of one or more “hidden” layers. Let’s dig in!

Visualization 72

Visualization Modeling Machine Learning IT 72

Domino Data Lab

AUGUST 22, 2019

Introduction. Introduction. Data scientists and researchers require an extensive array of techniques, packages, and tools to accelerate core work flow tasks including prepping, processing, and analyzing data. Utilizing NLP helps researchers and data scientists complete core tasks faster.

Deep Learning 111

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