IBM Big Data Hub

FEBRUARY 23, 2024

Machine learning (ML) and deep learning (DL) form the foundation of conversational AI development. DL models can improve over time through further training and exposure to more data. These technologies enable systems to interact, learn from interactions, adapt and become more efficient. billion by 2030.

Enterprise 105

Enterprise Interactive Data-driven Key Performance Indicator 105

O'Reilly on Data

JUNE 18, 2019

There has been a significant increase in our ability to build complex AI models for predictions, classifications, and various analytics tasks, and there’s an abundance of (fairly easy-to-use) tools that allow data scientists and analysts to provision complex models within days. Data integration and cleaning.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

IBM Big Data Hub

FEBRUARY 14, 2024

Other uses include Netflix offering viewing recommendations powered by models that process data sets collected from viewing history; LinkedIn uses ML to filter items in a newsfeed, making employment recommendations and suggestions on who to connect with; and Spotify uses ML models to generate its song recommendations.

Cost-Benefit 89

Cost-Benefit Insurance Unstructured Data Machine Learning 89

IBM Big Data Hub

AUGUST 28, 2023

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.

Data mining 59

Data mining Machine Learning Deep Learning Marketing 59

DataRobot

OCTOBER 20, 2021

Data literacy ensures that diverse perspectives are baked into AI governance and leads to the production of AI systems that achieve better and more consistent outcomes. It is a key component of developing responsible AI, and promotes trust not only in AI concepts but also in individual AI models. What Is Data Literacy?

Machine Learning 52

Machine Learning Metrics Risk Deep Learning 52

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 332

Management Machine Learning Experimentation Metrics 332

Decision Management Solutions

FEBRUARY 8, 2021

As you’ll see, the development of this amazing, one-of-a-kind vessel led to a conclusion that we at Decision Management Solutions see every day in our client work: It’s never enough to just rely on artificial intelligence (AI)/machine learning (ML) to do all the decision-making.

Machine Learning 93

Machine Learning Testing Deep Learning Optimization 93

Occam's Razor

MARCH 30, 2017

People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Most Deep Learning methods involve artificial neural networks, modeling how our bran works. There won’t be any need for them.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Insight

JANUARY 14, 2020

a “pre-trained” model) to be recycled and reused for many different tasks. In short, I was faced with two major difficulties regarding data collection: I didn’t have nearly enough images, and the images I did have were not representative of a realistic gym environment. We pass 3 parameters: loss, optimizer , and metrics.

Metrics 58

Metrics Optimization Modeling Testing 58

Rocket-Powered Data Science

JULY 13, 2023

Beyond the early days of data collection, where data was acquired primarily to measure what had happened (descriptive) or why something is happening (diagnostic), data collection now drives predictive models (forecasting the future) and prescriptive models (optimizing for “a better future”).

Analytics 166

Analytics Machine Learning Data Science Deep Learning 166

Domino Data Lab

JULY 22, 2019

data science’s emergence as an interdisciplinary field – from industry, not academia. why data governance, in the context of machine learning is no longer a “dry topic” and how the WSJ’s “global reckoning on data governance” is potentially connected to “premiums on leveraging data science teams for novel business cases”.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

IBM Big Data Hub

MAY 17, 2023

They use drones for tasks as simple as aerial photography or as complex as sophisticated data collection and processing. It can offer data on demand to different business units within an organization, with the help of various sensors and payloads. The global commercial drone market is projected to grow from USD 8.15

Data Collection 86

Data Collection Visualization Data-driven Unstructured Data 86

IBM Big Data Hub

NOVEMBER 14, 2023

Data monetization is not narrowly “selling data sets ;” it is about improving work and enhancing business performance by better-using data. External monetization opportunities enable different types of data in different formats to be information assets that can be sold or have their value recorded when used.

Strategy 114

Strategy Cost-Benefit Data-driven Measurement 114

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Introduction.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

MARCH 3, 2019

Then, when we received 11,400 responses, the next step became obvious to a duo of data scientists on the receiving end of that data collection. Over the past six months, Ben Lorica and I have conducted three surveys about “ABC” (AI, Big Data, Cloud) adoption in enterprise. Who builds their models?

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

JUNE 23, 2019

It used deep learning to build an automated question answering system and a knowledge base based on that information. It is like the Google knowledge graph with all those smart, intelligent cards and the ability to create your own cards out of your own data. There’s no substitute for that experience.

Management 110

Management Machine Learning Experimentation Metrics 110

datapine

NOVEMBER 19, 2019

There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. 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

Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. The interest in interpretation of machine learning has been rapidly accelerating in the last decade.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

Occam's Razor

OCTOBER 12, 2017

Optimal Starting SCOTUS Starting Points. If you would like to pursue my personal strategy, here are a collection of cases to use as starting points. Intro to Machine Learning. Machine Learning. Deep Learning. A good example of this Justice Scalia's opinion in Gonzales v.

Marketing 136

Marketing Analytics Machine Learning Strategy 136