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Biggest Trends in Data Visualization Taking Shape in 2022

Smart Data Collective

There are countless examples of big data transforming many different industries. It can be used for something as visual as reducing traffic jams, to personalizing products and services, to improving the experience in multiplayer video games. This is something that you can learn more about in just about any technology blog.

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Improving Signal Classification using Visual AI

DataRobot

When we convert the single channel audio signal time series into an energy spectrogram, it allows us to run state of the art deep learning architectures on the image. . Spectrograms are not the only transformations available to convert signal data to images. Image courtesy towardsAI. See DataRobot in Action. Request a Demo.

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Document Information Extraction Using Pix2Struct

Analytics Vidhya

Introduction Document information extraction involves using computer algorithms to extract structured data (like employee name, address, designation, phone number, etc.) from unstructured or semi-structured documents, such as reports, emails, and web pages.

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Anomaly detection in machine learning: Finding outliers for optimization of business functions

IBM Big Data Hub

The main difference being that while KNN makes assumptions based on data points that are closest together, LOF uses the points that are furthest apart to draw its conclusions. Unsupervised learning Unsupervised learning techniques do not require labeled data and can handle more complex data sets.

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Top Data Science Tools That Will Empower Your Data Exploration Processes

datapine

Data science has become an extremely rewarding career choice for people interested in extracting, manipulating, and generating insights out of large volumes of data. To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations.

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Capgemini and IBM Ecosystem strengthen partnership for Drone-as-a-Service

IBM Big Data Hub

The services are activated through access management for data collection, analysis and event monitoring in existing drones which are managed by clients and businesses. The flexibility of DaaS in offering a multiplicity of data collection services for different industry use cases makes it unique.

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Data science vs. machine learning: What’s the difference?

IBM Big Data Hub

The fields have evolved such that to work as a data analyst who views, manages and accesses data, you need to know Structured Query Language (SQL) as well as math, statistics, data visualization (to present the results to stakeholders) and data mining. Machine learning and deep learning are both subsets of AI.