IBM Big Data Hub

APRIL 18, 2024

While cost wasn’t the primary driver, it reflects a growing belief that the value generated by gen AI outweighs the price tag. Connectionist AI (artificial neural networks): This approach is inspired by the structure and function of the human brain.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

IBM Big Data Hub

AUGUST 28, 2023

Part-of-speech (POS) tagging: POS tagging facilitates semantic analysis by assigning grammatical tags to words (e.g., Machine learning algorithms like Naïve Bayes and support vector machines (SVM), and deep learning models like convolutional neural networks (CNN) are frequently used for text classification.

Data mining 58

Data mining Machine Learning Deep Learning Marketing 58

O'Reilly on Data

MARCH 7, 2019

Not only will named entity recognition or entity resolution models fail, but even basic tasks such as tokenization , part of speech tagging , and sentence segmentation will fail for the majority of sentences. Neural network graphs. Health care-specific part-of-speech tagging. Medical embeddings. NLP Pipeline API’s.

Deep Learning 227

Deep Learning Testing Machine Learning Modeling 227

IBM Big Data Hub

FEBRUARY 14, 2024

Companies also take advantage of ML in smartphone cameras to analyze and enhance photos using image classifiers, detect objects (or faces) in the images, and even use artificial neural networks to enhance or expand a photo by predicting what lies beyond its borders.

Cost-Benefit 88

Cost-Benefit Insurance Unstructured Data Machine Learning 88

Ontotext

FEBRUARY 14, 2024

One such example is the tagging behind News On the Web. A combination of the above: CEEL computes a combined score by concatenation of the features for the entity typing score, entity description score for the candidates and passing them through a set of neural network layers. This approach has certain benefits.

Cost-Benefit 52

Cost-Benefit Modeling Metadata Optimization 52

Dataiku

MARCH 25, 2024

At their core, M-LLMs consist of versatile neural networks capable of ingesting various data types, thereby gaining insights across different modalities. This text information includes tags, attributes, captions. M-LLM M-LLMs exhibit remarkable proficiency across various real-world tasks.

Visualization 132

Visualization Modeling Experimentation Testing 132

Smart Data Collective

MARCH 26, 2021

It entails deep learning from its neural networks, natural language processing (NLP), and constant changes based on incoming information. Manually, it would take individuals countless hours to sort types of images into folders or tags. Then, they can help people in daily life. Machine learning mimics the human brain.

Machine Learning 140

Machine Learning Interactive Deep Learning Technology 140

IBM Big Data Hub

OCTOBER 16, 2023

ML analyzes and enhances photos using image classifiers, detects objects (or faces) in the images, and can even use artificial neural networks to enhance or expand a photo by predicting what lies beyond its borders. Then, it suggests the social media user tag that individual.

Machine Learning 51

Machine Learning Marketing Forecasting Modeling 51

O'Reilly on Data

JULY 28, 2020

Nor do I believe there’s some kind of racist demon lurking in the mathematics implemented by neural networks. And why are we not surprised that these issues show up all the time, in applications ranging from COMPAS to the Google app that tagged Black people as gorillas? They are predominantly White and male.

Testing 135

Testing Statistics Recreation/Entertainment Software 135

O'Reilly on Data

MARCH 22, 2019

Machines only follow instructions, whether those instruction are concrete rules or the arcane computations of a neural network. Emails tagged as spam aren’t deleted for 30 days; at any time during that period, the user can visit the spam folder and say “that’s not spam.”

Metadata 241

Metadata Advertising Insurance Modeling 241

Smart Data Collective

JUNE 4, 2021

Deep learning algorithms use artificial neural networks to solve problems. Facebook and google photos identify people from images and recommend tags. Use deep learning when the number of variables (columns) is high. Deep learning is used for speech recognition, board games AI, image recognition, and manipulation.

Data Science 104

Data Science Statistics Deep Learning Machine Learning 104

The Data Visualisation Catalogue

JANUARY 24, 2023

It uses a transformer-based neural network architecture and is trained on a gigantic dataset of human-generated text. After consuming a number of YouTube videos, blog posts, articles, and playing around with ChatGPT, I felt the need to write down my thoughts and observations on the topic.

Visualization 112

Visualization Interactive Data-driven Testing 112

Domino Data Lab

JULY 1, 2021

This dataset is based on the GMB ( Groningen Meaning Bank ) corpus, and has been tagged, annotated and built specifically to train a classifier to predict named entities such as name, location, etc. The tags used in the dataset follow the IOB format, which we cover in the next section. The IOB format. a noun group, a verb group etc.)

Modeling 111

Modeling Statistics Testing Metrics 111

Occam's Razor

MARCH 30, 2017

Notice nothing is categorized or tagged in the pictures above. Most Deep Learning methods involve artificial neural networks, modeling how our bran works. For example, I want pictures of Sushi… I simply type in Sushi… Boom! Clearly I’m not your classic I love my food so much I must Instagram every meal person. :).

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Sisense

AUGUST 6, 2020

One proven method for success is to refine preferences in real time by inserting new customer choices into the neural net during transactions. Getting this AI properly trained required a huge learning dataset with countless documents that were tagged according to specific criteria. Look at implicit and explicit feedback.

Management 95

Management Machine Learning Key Performance Indicator Cost-Benefit 95

IBM Big Data Hub

SEPTEMBER 20, 2023

Large language models (LLMs) are a class of foundational models (FM) that consist of layers of neural networks that have been trained on these massive amounts of unlabeled data. These massive amounts of data that exist in every business are waiting to be unleashed to drive insights. What are large language models?

Modeling 90

Modeling Enterprise Software Machine Learning 90

Domino Data Lab

MARCH 6, 2019

The conference topics and lineup of speakers ranged from: Cutting-edge Deep Learning Techniques, with Dr. Frank Ferraro from UMBC on Neural Semantic Methods for Approachable Fact Prediction and Verification and Google’s Dr. Marc Pickett on Continual Learning. A line is only a line because spatially related pixels form the line.

Deep Learning 55

Deep Learning Data Science Machine Learning Modeling 55

Sisense

MAY 26, 2020

Preprocessing of data means removal of unwanted URLs, HTML tags, stop words, numeric and alphanumeric words, punctuation, and special characters. Also deep learning models like recurrent neural networks help machines to understand the context of the words used. It also involves converting all text to lowercase.

Unstructured Data 98

Unstructured Data Big Data Testing Machine Learning 98