Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Scale the problem to handle complex data structures. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Data Science Machine Learning Data-driven 105

Domino Data Lab

MARCH 16, 2021

2018) Simple meaningless data processing steps, may cause saliency methods to result in significant changes (Kindermans et al., DeepLIFT was recently proposed as a recursive prediction explanation method for deep learning [8, 7]. This is an exciting and important area of data science research. Saliency Maps.

Modeling 122

Modeling Deep Learning Testing Optimization 122

CIO Business Intelligence

AUGUST 11, 2023

Lilly Translate uses NLP and deep learning language models trained with life sciences and Lilly content to provide real-time translation of Word, Excel, PowerPoint, and text for users and systems. Licensed by MIT, SpaCy was made with high-level data science in mind and allows deep data mining.

Unstructured Data 142

Unstructured Data Machine Learning Data Science Data mining 142

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

A more general approach is to learn a Generalized Additive Model (GAM). GAMs are popular among data science and machine learning applications for their simplicity and interpretability. Monotonic Deep Lattice Networks Deep learning is a powerful tool when we have an abundance of data to learn from.

Machine Learning 111

Machine Learning Deep Learning Modeling Interactive 111

Domino Data Lab

SEPTEMBER 9, 2019

Data science teams in industry must work with lots of text, one of the top four categories of data used in machine learning. That’s excellent for supporting really interesting workflow integrations in data science work. Usually it’s human-generated text, but not always.

Deep Learning 110

Deep Learning Machine Learning Data Science Visualization 110

CIO Business Intelligence

APRIL 15, 2022

In 2017, The Economist declared that data, rather than oil, had become the world’s most valuable resource. Organizations across every industry have been and continue to invest heavily in data and analytics. But like oil, data and analytics have their dark side. The refrain has been repeated ever since.

Analytics 145

Analytics Machine Learning Insurance Testing 145

Domino Data Lab

APRIL 3, 2019

The top three items are essentially “the devil you know” for firms which want to invest in data science: data platform, integration, data prep. Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Rinse, lather, repeat.

Machine Learning 84

Machine Learning Data Governance Metadata Data Science 84

Insight

FEBRUARY 13, 2019

State-of-the-art pre-training for natural language processing with BERT Javed Qadrud-Din was an Insight Fellow in Fall 2017. He is currently a machine learning engineer at Casetext where he works on natural language processing for the legal industry. Prior to Insight, he was at IBM Watson.

Testing 72

Testing Machine Learning Deep Learning Modeling 72

Insight

OCTOBER 25, 2019

Image credit: [link] As the title suggests, this is a story about a question that may resonate well with many machine learning practitioners trying to build applications in the real world, where clean and annotated data on a specific problem can be sparse— How do we leverage the power of AI when we have very little data?

Snapshot 57

Snapshot Testing Deep Learning Visualization 57

Domino Data Lab

FEBRUARY 10, 2019

Humans likely not even notice the difference but modern deep learning networks suffered a lot. But apparently, models trained on text from 2017 experience degraded performance on text written in 2018. Images off the web tend to frame the object in question. We might expect that.

Machine Learning 63

Machine Learning Modeling Deep Learning Metrics 63

O'Reilly on Data

MARCH 19, 2020

Our analysis of ML- and AI-related data from the O’Reilly online learning platform indicates: Unsupervised learning surged in 2019, with usage up by 172%. Deep learning cooled slightly in 2019, slipping 10% relative to 2018, but deep learning still accounted for 22% of all AI/ML usage.

Deep Learning 356

Deep Learning Machine Learning Data-driven Modeling 356

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Machine learning model interpretability. Adrian Weller (2017-07-29). “ Riccardo Guidotti, et al.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena.

Big Data 25

Big Data IoT Data Warehouse Uncertainty 25

Domino Data Lab

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning. References.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Insight

DECEMBER 30, 2019

In Figure 1, you can see the results of the Harris corner detector applied to an image of Jessie Graff competing in the 2017 American Ninja Warrior Finals: Figure 1?—?Harris Harris corner detection applied to an image of Jessie Graff competing in the 2017 American Ninja Warrior Finals. Original image is on the left.

Visualization 72

Visualization Machine Learning Modeling IT 72

O'Reilly on Data

JANUARY 25, 2021

Our data shows that Chef and Puppet peaked in 2017, when Kubernetes started an almost exponential growth spurt, as Figure 4 shows. AI, Machine Learning, and Data. Healthy growth in artificial intelligence has continued: machine learning is up 14%, while AI is up 64%; data science is up 16%, and statistics is up 47%.

Machine Learning 364

Machine Learning Software Testing Technology 364