Analytics Vidhya

JULY 12, 2021

ArticleVideo Book This article was published as a part of the Data Science Blogathon Introduction In this blog, we will understand how to create and. The post Classification of Handwritten Digits Using CNN appeared first on Analytics Vidhya.

Data Science 296

Data Science Publishing Analytics Unstructured Data 296

Analytics Vidhya

SEPTEMBER 6, 2021

This article was published as a part of the Data Science Blogathon Image 1 Introduction We have explored the Pipeline API of the transformers library which can be used for quick inference tasks. You can find more about it in my previous blog post here. Now let’s go deep dive into the Transformers library and […].

Data Science 295

Data Science Publishing Analytics Unstructured Data 295

IBM Big Data Hub

JULY 6, 2023

Python is the most common programming language used in machine learning. Machine learning and deep learning are both subsets of AI. Deep learning teaches computers to process data the way the human brain does. Deep learning algorithms are neural networks modeled after the human brain.

Machine Learning 60

Machine Learning Data Science Statistics Deep Learning 60

datapine

AUGUST 14, 2019

Data science tools are used for drilling down into complex data by extracting, processing, and analyzing structured or unstructured data to effectively generate useful information while combining computer science, statistics, predictive analytics, and deep learning.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

IBM Big Data Hub

JULY 6, 2023

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. This blog post will clarify some of the ambiguity. Machine learning is a subset of AI.

Deep Learning 61

Deep Learning Machine Learning Unstructured Data Modeling 61

IBM Big Data Hub

FEBRUARY 14, 2024

Financial services AI-powered FinOps (Finance + DevOps) helps financial institutions operationalize data-driven cloud spend decisions to safely balance cost and performance in order to minimize alert fatigue and wasted budget. AI platforms can use machine learning and deep learning to spot suspicious or anomalous transactions.

Cost-Benefit 88

Cost-Benefit Insurance Unstructured Data Machine Learning 88

IBM Big Data Hub

AUGUST 28, 2023

Named entity recognition (NER): NER extracts relevant information from unstructured data by identifying and classifying named entities (like person names, organizations, locations and dates) within the text. And with advanced software like IBM Watson Assistant , social media data is more powerful than ever.

Data mining 58

Data mining Machine Learning Deep Learning Marketing 58

bridgei2i

OCTOBER 7, 2020

To get a perspective on evolving trends in insurance in the post-COVID world, read BRIDGEi2i’s blog here. Underwriting is a process that involves extraction & collation of information about the insured from different sources in structured & unstructured format. (if click here. Challenges in underwriting.

Insurance 52

Insurance Sales Strategy Digital Transformation 52

Domino Data Lab

AUGUST 9, 2021

Before selecting a tool, you should first know your end goal – machine learning or deep learning. Machine learning identifies patterns in data using algorithms that are primarily based on traditional methods of statistical learning. It’s most helpful in analyzing structured data.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 111

IBM Big Data Hub

JANUARY 10, 2024

Algorithms: Algorithms are the sets of rules AI systems use to process data and make decisions. The category of AI algorithms includes ML algorithms, which learn and make predictions and decisions without explicit programming. Traditionally coded programs also struggle with independent iteration.

Unstructured Data 84

Unstructured Data Deep Learning Cost-Benefit Modeling 84

Sisense

MAY 26, 2020

In Talking Data , we delve into the rapidly evolving worlds of Natural Language Processing and Generation. Text data is proliferating at a staggering rate, and only advanced coding languages like Python and R will be able to pull insights out of these datasets at scale. Today, text data is everywhere.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98

IBM Big Data Hub

AUGUST 1, 2022

As a company, we have been entrusted with organizing data on a national scale, made revolutionary progress in data storing technology and have exponentially advanced trustworthy AI using aggregated structured and unstructured data from both internal and external sources. . 2000 Deep Learning: .

Unstructured Data 52

Unstructured Data Deep Learning Data Quality Data Architecture 52

Domino Data Lab

AUGUST 10, 2021

PyTorch: used for deep learning models, like natural language processing and computer vision. It’s used for developing deep learning models. Horovod: is a distributed deep learning training framework that can be used with PyTorch, TensorFlow, Keras, and other tools.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Ontotext

MAY 22, 2023

Luckily, the text analysis that Ontotext does is focused on tasks that require complex domain knowledge and linking of documents to reference data or master data. We use other deep learning techniques for such tasks. That’s something that LLMs cannot do.

Management 59

Management Unstructured Data Metadata Cost-Benefit 59

DataKitchen

APRIL 13, 2021

Read the complete blog below for a more detailed description of the vendors and their capabilities. This is not surprising given that DataOps enables enterprise data teams to generate significant business value from their data. Download the 2021 DataOps Vendor Landscape here. DataOps is a hot topic in 2021.

Testing 307

Testing Machine Learning Consulting Data Quality 307

DataRobot

NOVEMBER 9, 2021

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.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

IBM Big Data Hub

AUGUST 1, 2023

It’s the underlying engine that gives generative models the enhanced reasoning and deep learning capabilities that traditional machine learning models lack. Visit the watsonx webpage to learn more The post How foundation models and data stores unlock the business potential of generative AI appeared first on IBM Blog.

Modeling 60

Modeling Cost-Benefit Data Lake Machine Learning 60

IBM Big Data Hub

MAY 17, 2023

The data captured by the sensors and housed in the cloud flow into real-time monitoring for 24/7 visibility into your assets, enabling the Predictive Failure Model. DaaS uses built-in deep learning models that learn by analyzing images and video streams for classification.

Data Collection 83

Data Collection Visualization Data-driven Unstructured Data 83

datapine

NOVEMBER 19, 2019

An important part of artificial intelligence comprises machine learning, and more specifically deep learning – that trend promises more powerful and fast machine learning. An exemplary application of this trend would be Artificial Neural Networks (ANN) – the predictive analytics method of analyzing data.

Technology 279

Technology Internet of Things IT IoT 279

Ontotext

FEBRUARY 26, 2024

Model-driven extraction Model-driven extraction refers to the extraction and classification of unstructured data sources, such as text documents and web pages, based on a predefined schema, often referred to as a semantic model. This semantic model serves as a blueprint or framework against which raw data is analyzed and organized.

Data-driven 59

Data-driven Risk Modeling Risk Management 59

Smart Data Collective

OCTOBER 13, 2021

There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. This is something that you can learn more about in just about any technology blog. We would like to talk about data visualization and its role in the big data movement.

Visualization 135

Visualization Cost-Benefit Big Data Prescriptive Analytics 135

CDW Research Hub

SEPTEMBER 15, 2020

Traditional data warehouses are often too slow and can’t handle large volumes of data or different types of semi-structured or unstructured data. They can be inflexible and costly since you are not able to scale your usage as you would using a modern data platform and the cloud.

Business Intelligence 52

Business Intelligence Prescriptive Analytics Unstructured Data Data Warehouse 52