The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

Monotonic Deep Lattice Networks Deep learning is a powerful tool when we have an abundance of data to learn from. In this section, we extend the ideas of building monotonic GAMs and lattice models to construct monotonic deep learning models. Other deep learning models can also be written in this form.

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DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. ParallelM — Moves machine learning into production, automates orchestration, and manages the ML pipeline. Acquired by DataRobot June 2019). Meta-Orchestration .

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Testing Machine Learning Consulting Data Quality 304

FineReport

DECEMBER 19, 2019

At the same time, it also advocates visual exploratory analysis. The visualization component library of FineReport is very rich. Pandas incorporates a large number of analysis function methods, as well as common statistical models and visualization processing. It is recommended that everyone learn to learn.

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KDnuggets

OCTOBER 14, 2019

Also: Activation maps for deep learning models in a few lines of code; The 4 Quadrants of Data Science Skills and 7 Principles for Creating a Viral Data Visualization; OpenAI Tried to Train AI Agents to Play Hide-And-Seek but Instead They Were Shocked by What They Learned; 10 Great Python Resources for Aspiring Data Scientists.

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O'Reilly on Data

DECEMBER 12, 2019

If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. Figure 1 illustrates an example adversarial search for an example credit default ML model. The main drawback of residual analysis is that to calculate residuals, true outcomes are needed.

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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.

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Domino Data Lab

MAY 8, 2019

On the other hand, as Lipton emphasized, while the tooling produces interesting visualizations, visualizations do not imply interpretation. ML model interpretability and data visualization. From my experiences leading data teams, when a business is facing difficult challenges, data visualizations can help or hurt.

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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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bridgei2i

APRIL 13, 2020

She goes on to add that ‘Understanding vision and building visual systems are really about understanding intelligence.’ billion in 2019 to $7.0 Stanford University’s leading AI Scientist Fei-Fei Li feels that vision is the most competitive cognitive ability that can change the course of AI. billion by 2024 in the US.

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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).

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Cloudera

FEBRUARY 4, 2019

Flow Management – Adopt a no-code approach to create visual flows for building complex data ingestion / transformation with drag-and-drop ease. To learn more about Cloudera DataFlow, attend our upcoming webinar on Feb 13th, 2019. He is fascinated by new technology trends including blockchain and deep learning.

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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. For accurate predictions, companies now use various data models, machine and deep learning techniques to continuously improve and refine the quality of the outcome. Source: Gartner Research).

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KDnuggets

NOVEMBER 28, 2019

Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.

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KDnuggets

SEPTEMBER 17, 2019

We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.

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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. We keep feeding the monster data.

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Domino Data Lab

OCTOBER 9, 2019

The web site provides tools to visualize and download specific signals and time ranges: One of my favorite sessions at EDM (as one might imagine) was about Metadata Tools. AI CA 2019 highlights. 2010s—focused on “pattern recognition” as in deep learning used for computer vision or speech-to-text. How cool is that?!

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Business Over Broadway

DECEMBER 20, 2018

Top Machine Learning and Data Science Methods Used at Work – The practice of data science requires the use algorithms and data science methods to help data professionals extract insights and value from data. Looking ahead to 2018, data professionals are most interested in learning deep learning (41%).

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KDnuggets

OCTOBER 31, 2019

These results will go into each each region and employment type to find out the differences and similarities especially between people from Industry and Students.

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Domino Data Lab

SEPTEMBER 9, 2019

Next let’s use the displaCy library to visualize the parse tree for that sentence: In [4]: from spacy import displacy?? The displaCy library provides an excellent way to visualize named entities: In [15]: displacy.render(doc, style="ent"). lemma – a root form of the word. part of speech. Out[14]: Steve Jobs PERSON?

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