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Minerva – Google’s Language Model for Quantitative Reasoning

Analytics Vidhya

This article was published as a part of the Data Science Blogathon. The model for natural language processing is called Minerva. Recently, experimenters have developed a very sophisticated natural language […]. Recently, experimenters have developed a very sophisticated natural language […].

Modeling 359
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End to End Statistics for Data Science

Analytics Vidhya

This article was published as a part of the Data Science Blogathon Introduction to Statistics Statistics is a type of mathematical analysis that employs quantified models and representations to analyse a set of experimental data or real-world studies. Data processing is […].

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When models are everywhere

O'Reilly on Data

Not all models are created equal, however: they operate on different principles, and impact us as individuals and communities in different ways. To understand the menagerie of models that are fundamentally altering our individual and shared realities, we need to build a typology, a classification of their effects and impacts.

Modeling 188
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10 Technical Blogs for Data Scientists to Advance AI/ML Skills

DataRobot Blog

Other organizations are just discovering how to apply AI to accelerate experimentation time frames and find the best models to produce results. Taking a Multi-Tiered Approach to Model Risk Management. Learn how to leverage Google BigQuery large datasets for large scale Time Series forecasting models in the DataRobot AI platform.

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Of Muffins and Machine Learning Models

Cloudera

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

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New DataRobot and Snowflake Integrations: Seamless Data Prep, Model Deployment, and Monitoring

DataRobot Blog

release, we’ve made it easy for you to rapidly prepare data, engineer new features and subsequently automate model deployment and monitoring into your Snowflake data landscape, all with limited data movement. We’ve tightened the loop between ML data prep , experimentation and testing all the way through to putting models into production.

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What you need to know about product management for AI

O'Reilly on Data

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.