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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model.

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Cloudera AI Inference Service Enables Easy Integration and Deployment of GenAI Into Your Production Environments

Cloudera

To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput. Teams can analyze the data using any BI tool for model monitoring and governance purposes. Data teams can use any metrics dashboarding tool to monitor these.

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Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

Customer purchase patterns, supply chain, inventory, and logistics represent just a few domains where we see new and emergent behaviors, responses, and outcomes represented in our data and in our predictive models. 7) Deep learning (DL) may not be “the one algorithm to dominate all others” after all.

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Why you should care about debugging machine learning models

O'Reilly on Data

More structured approaches to sensitivity analysis include: Adversarial example searches : this entails systematically searching for rows of data that evoke strange or striking responses from an ML model. Figure 1 illustrates an example adversarial search for an example credit default ML model.

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R vs Python: What’s the Best Language for Natural Language Processing?

Sisense

Using XG-Boost to model the text data resulted in an almost identical score for Python and R. There are many performance metrics to evaluate performance of Machine Learning models. This metric can be used in classification analyses to identify a model’s ability to predict a desired attribute, based on the training data.

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Better Forecasting with AI-Powered Time Series Modeling

DataRobot Blog

While AI-powered forecasting can help retailers implement sales and demand forecasting—this process is very complex, and even highly data-driven companies face key challenges: Scale: Thousands of item combinations make it difficult to manually build predictive models. A variety of models are been trained in parallel.

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AI In Analytics: Today and Tomorrow!

Smarten

Anomaly Alerts KPI monitoring and Auto Insights allows business users to quickly establish KPIs and target metrics and identify the Key Influencers and variables for the target KPI.