Remove Deep Learning Remove Machine Learning Remove Optimization Remove Structured Data
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AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?

IBM Big Data Hub

To keep up with the pace of consumer expectations, companies are relying more heavily on machine learning algorithms to make things easier. How do artificial intelligence, machine learning, deep learning and neural networks relate to each other? Machine learning is a subset of AI.

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Leveraging user-generated social media content with text-mining examples

IBM Big Data Hub

Text mining —also called text data mining—is an advanced discipline within data science that uses natural language processing (NLP) , artificial intelligence (AI) and machine learning models, and data mining techniques to derive pertinent qualitative information from unstructured text data.

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The Rise of Unstructured Data

Cloudera

In terms of representation, data can be broadly classified into two types: structured and unstructured. Structured data can be defined as data that can be stored in relational databases, and unstructured data as everything else. Here we briefly describe some of the challenges that data poses to AI.

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Breaking down the advantages and disadvantages of artificial intelligence

IBM Big Data Hub

Data: AI systems learn and make decisions based on data, and they require large quantities of data to train effectively, especially in the case of machine learning (ML) models. For optimal performance, AI models should receive data from a diverse datasets (e.g.,

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Building a Beautiful Data Lakehouse

CIO Business Intelligence

As a result, users can easily find what they need, and organizations avoid the operational and cost burdens of storing unneeded or duplicate data copies. Newer data lakes are highly scalable and can ingest structured and semi-structured data along with unstructured data like text, images, video, and audio.

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Themes and Conferences per Pacoid, Episode 7

Domino Data Lab

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

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Take advantage of AI and use it to make your business better

IBM Big Data Hub

Diving deeper, the potential of AI systems is also challenging us to go beyond these tools and think bigger: How will the application of AI and machine learning models advance big-picture, strategic business goals? How will you empower teams to make use of your data? The first of which, Slate , was recently released.

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