The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

If we observe label vector y and feature vectors $x_1, cdots, x_d$ we can write the differentiable empirical risk minimization problem with a squared loss as$$ min_theta left| y - sum_{j=1}^d c_j(x_j) right|^2 $$Note that we use squared loss for the simplicity of presentation; one can use any differentiable loss in their application.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

CIO Business Intelligence

JANUARY 23, 2023

In July 2019 it became OpenAI’s exclusive cloud provider and invested $1 billion in the company to support its quest to create “artificial general intelligence.” the OpenAI model on which ChatGPT is based, is an example of a transformer, a deep learning technique developed by Google in 2017 to tackle problems in natural language processing.

Enterprise 130

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Smart Data Collective

MARCH 16, 2022

In other cases, advanced AI applications use a deep-learning approach to sift through big data to predict the prices of stocks in the near future. However, deep-learning approaches are comprehensive in theory. In fact, by 2019, it is estimated that 2.1 Location data can be part of an analytical effort.

Big Data 104

Big Data Deep Learning Finance Data-driven 104

CIO Business Intelligence

JUNE 7, 2022

Those numbers represent the projected growth of chatbot interactions among banking customers between 2019 to 2023 and the cost savings from 862 hours less of work by support personnel, according to research by Juniper Research. In business, when a trend is forecast to grow by more than 3000% and generate cost savings of $7.3

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CIO Business Intelligence

APRIL 15, 2022

Derek Driggs, a machine learning researcher at the University of Cambridge, together with his colleagues, published a paper in Nature Machine Intelligence that explored the use of deep learning models for diagnosing the virus. The algorithm learned to identify children, not high-risk patients.

Analytics 141

Analytics Machine Learning Insurance Testing 141

O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. 2] The Security of Machine Learning. [3] That’s where model debugging comes in.

Machine Learning 187

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Domino Data Lab

OCTOBER 9, 2019

In some cases, their work is vital when millions of lives may be at risk due to natural disasters. We had working sessions about how to retrofit the saildrones with low-power ML on edge devices, using transfer learning with deep learning models to detect and avoid hazards from sea ice. AI CA 2019 highlights.

Deep Learning 69

Deep Learning Metadata Machine Learning Data Science 69

O'Reilly on Data

JANUARY 25, 2021

Year-over-year (YOY) growth compares January through September 2020 with the same months of 2019. Let’s look at the data, starting at the highest level: O’Reilly online learning itself. O’Reilly Online Learning. Usage of O’Reilly online learning grew steadily in 2020, with 24% growth since 2019.

Machine Learning 362

Machine Learning Software Testing Technology 362

BizAcuity

MARCH 31, 2022

of organizations who participated in an executive survey back in 2019 claimed they are going to be investing in big data and AI. 85% of AI (marketing) projects fail due to risk, confusion, and lack of upskilling among marketing teams. (Source: Gartner Research). Source: TCS). Source: Gartner Research). AI in Supply chain and Logistics.

Data Analytics 52

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O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks. Watermark attacks. Newer types of fair and private models (e.g.,

Modeling 219

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Domino Data Lab

MARCH 3, 2019

O’Reilly Media published our analysis as free mini-books: The State of Machine Learning Adoption in the Enterprise (Aug 2018). Evolving Data Infrastructure: Tools and Best Practices for Advanced Analytics and AI (Jan 2019). AI Adoption in the Enterprise: How Companies Are Planning and Prioritizing AI Projects in Practice (Feb 2019).

Data Science 49

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Domino Data Lab

JULY 2, 2019

See also: Caroline Lemieux’s slides for that NeurIPS talk, and Rohan Bavishi’s video from the RISE Summer Retreat 2019. The authors of AutoPandas observed that: The APIs for popular data science packages tend to have relatively steep learning curves. Program Synthesis 101 ” – Alexander Vidiborskiy (2019-01-20).

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Metadata Machine Learning Data Science Data-driven 105

O'Reilly on Data

SEPTEMBER 11, 2019

Our call for speakers for Strata NY 2019 solicited contributions on the themes of data science and ML; data engineering and architecture; streaming and the Internet of Things (IoT); business analytics and data visualization; and automation, security, and data privacy. Deep learning,” for example, fell year over year to No.

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Smart Data Collective

OCTOBER 14, 2019

Ludwig is a tool that allows people to build data-based deep learning models to make predictions. In September 2019, Google decided to make it’s Differential Privacy Library available as an open-source tool. Here are some open-source options to consider. You don’t even need coding knowledge to get started with it.

Data Science 67

Data Science Machine Learning Big Data Interactive 67

Domino Data Lab

MAY 8, 2019

That’s a risk in case, say, legislators – who don’t understand the nuances of machine learning – attempt to define a single meaning of the word interpret. 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.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

O'Reilly on Data

MARCH 31, 2020

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. Managing Machine Learning Projects” (AWS).

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Domino Data Lab

APRIL 3, 2019

Data governance shows up as the fourth-most-popular kind of solution that enterprise teams were adopting or evaluating during 2019. Plus, the more mature machine learning (ML) practices place greater emphasis on these kinds of solutions than the less experienced organizations. Rinse, lather, repeat. Those are table stakes in this game.

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Data Governance Machine Learning Metadata Big Data 84