CIO Business Intelligence

MAY 20, 2022

The Machine Learning Department at Carnegie Mellon University was founded in 2006 and grew out of the Center for Automated Learning and Discovery (CALD), itself created in 1997 as an interdisciplinary group of researchers with interests in statistics and machine learning. University of California–Berkeley.

Machine Learning 145

Machine Learning Statistics Recreation/Entertainment Data Science 145

datapine

AUGUST 14, 2019

To fully leverage the power of data science, scientists often need to obtain skills in databases, statistical programming tools, and data visualizations. It helps to automate and makes the usage of the R programming statistical language easier and much more effective. perfect for statistical computing and design.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

IBM Big Data Hub

FEBRUARY 13, 2024

Generative AI represents a significant advancement in deep learning and AI development, with some suggesting it’s a move towards developing “ strong AI.” This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task.

Enterprise 125

Enterprise Modeling Testing Data-driven 125

Domino Data Lab

JULY 28, 2021

Through a marriage of traditional statistics with fast-paced, code-first computer science doctrine and business acumen, data science teams can solve problems with more accuracy and precision than ever before, especially when combined with soft skills in creativity and communication. Math and Statistics Expertise.

Data Science 110

Data Science Statistics Recreation/Entertainment Predictive Analytics 110

Smart Data Collective

AUGUST 20, 2021

You need to know how to leverage machine learning algorithms appropriately. Machine Learning Technology Can Be Ideal for Better Monetizing Your Android Apps. The statistic shows that users routinely open 4-6 applications every day. You need to know how to leverage your machine learning algorithms effectively.

Machine Learning 89

Machine Learning Data mining Advertising Deep Learning 89

CIO Business Intelligence

AUGUST 11, 2023

Transformer models take applications such as language translation and chatbots to a new level. Innovations such as the self-attention mechanism and multi-head attention enable these models to better weigh the importance of various parts of the input, and to process those parts in parallel rather than sequentially.

Unstructured Data 142

Unstructured Data Modeling Machine Learning Data mining 142

Cloudera

FEBRUARY 25, 2021

Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. In our current landscape, businesses that have adopted a successful ML strategy are outperforming their competitors by over 9%. The work of a machine learning model developer is highly complex.

Deep Learning 83

Deep Learning Machine Learning Forecasting Visualization 83

bridgei2i

OCTOBER 7, 2020

Adoption of Automated Sales & Underwriting Strategies can Transform Insurance. Importance of capturing market data for optimized pricing models. The insurance industry—which, in the US alone, stands at $1.2 trillion, is seeing the volume of insurance transactions growing every year. The way ahead for insurers.

Insurance 52

Insurance Sales Strategy Digital Transformation 52

Rocket-Powered Data Science

JULY 6, 2023

This is not entirely due to or related to ChatGPT and LLMs (large language models), though those have been the main drivers. This year, artificial intelligence (AI) has become a major conversation centerpiece at home, in the park, at the gym, at work, everywhere.

Strategy 152

Strategy Enterprise Software Marketing 152

O'Reilly on Data

JULY 28, 2020

You must detect when the model has become stale, and retrain it as necessary. Products based on deep learning can be difficult (or even impossible) to develop; it’s a classic “high return versus high risk” situation, in which it is inherently difficult to calculate return on investment.

Marketing 362

Marketing Experimentation Metrics Testing 362

O'Reilly on Data

MARCH 31, 2020

This means that the AI products you build align with your existing business plans and strategies (or that your products are driving change in those plans and strategies), that they are delivering value to the business, and that they are delivered on time. We won’t go into the mathematics or engineering of modern machine learning here.

Management 332

Management Machine Learning Experimentation Metrics 332

Domino Data Lab

OCTOBER 7, 2020

Statistical methods for analyzing this two-dimensional data exist. This statistical test is correct because the data are (presumably) bivariate normal. When there are many variables the Curse of Dimensionality changes the behavior of data and standard statistical methods give the wrong answers. Data Has Properties.

Statistics 145

Statistics Testing Predictive Modeling Modeling 145

bridgei2i

FEBRUARY 21, 2020

In fact, statistics from Maryville University on Business Data Analytics predict that the US market will be valued at more than $95 billion by the end of this year. Deep learning provides an edge over your competition. ML-based tech such as NLP, Computer Vision, and Reinforcement Learning To Increase.

IoT 83

IoT Analytics Deep Learning B2B 83

Smart Data Collective

MAY 8, 2022

From the statistics shown, this means that both AI and big data have the potential to affect how we work in the workplace. By doing this, businesses can form their finance & marketing strategies with the new information they have gathered. As a result, it can improve how effective it is and help you set up better strategies.

Big Data 108

Big Data Data Analytics Analytics Cost-Benefit 108

IBM Big Data Hub

APRIL 18, 2024

While these large language model (LLM) technologies might seem like it sometimes, it’s important to understand that they are not the thinking machines promised by science fiction. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

Cost-Benefit 96

Cost-Benefit Modeling Manufacturing Interactive 96

Occam's Razor

MARCH 30, 2017

My personal strategy: Understand reality. Invest in continuous learning. People tend to use these phrases almost interchangeably: Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning. Deep Learning is a specific ML technique. Add new/different value. Rinse and repeat.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Domino Data Lab

DECEMBER 1, 2019

This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in production. Introduction: preventing silent model degradation in production. This article explores an approach that can be used to detect data drift for models that classify/score image data.

Modeling 77

Modeling Machine Learning Deep Learning Testing 77

O'Reilly on Data

MARCH 20, 2019

Apply fair and private models, white-hat and forensic model debugging, and common sense to protect machine learning models from malicious actors. Like many others, I’ve known for some time that machine learning models themselves could pose security risks.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

Domino Data Lab

AUGUST 10, 2021

Models are the central output of data science, and they have tremendous power to transform companies, industries, and society. At the center of every machine learning or artificial intelligence application is the ML/AI model that is built with data, algorithms and code. The process of creating models is called modeling.

Modeling 64

Modeling IT Risk Management Deep Learning 64

Smarten

APRIL 19, 2024

OpenAI – Azure OpenAI as the foundational entity for creating GPT models and is based on Large Language Models (LLM). GPT – Is based on a Large Language Model (LLM). Benefits include customized and optimized models, data, parameters and tuning. Open AI was developed by Microsoft.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

IBM Big Data Hub

DECEMBER 20, 2023

Machine learning (ML) technologies can drive decision-making in virtually all industries, from healthcare to human resources to finance and in myriad use cases, like computer vision , large language models (LLMs), speech recognition, self-driving cars and more. However, the growing influence of ML isn’t without complications.

Machine Learning 82

Machine Learning Modeling Deep Learning Predictive Modeling 82

Domino Data Lab

APRIL 21, 2021

We’ll use a gradient boosting technique via XGBoost to create a model and I’ll walk you through steps you can take to avoid overfitting and build a model that is fit for purpose and ready for production. deep learning) there is no guaranteed explainability. It can be implemented as either unsupervised (e.g.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

Domino Data Lab

DECEMBER 17, 2019

While Domino offers a platform where industry is able to leverage their choice of languages, tools and infra to support model-driven workflows, we are also committed to supporting data scientists and researchers in assessing whether a framework, like GANs, will help accelerate their work. This blog post provides high-level insights into GANs.

Deep Learning 64

Deep Learning Machine Learning Modeling Statistics 64

CONTACT Software

FEBRUARY 19, 2019

“Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, machine learning with stats models and scikit-learn, deep learning with TensorFlow” is one of Udemy’s course programs.

Data Science 40

Data Science Statistics Machine Learning Deep Learning 40

Domino Data Lab

MARCH 3, 2019

The first survey started as a simple exploration into mainstream adoption of machine learning (ML). What’s been the impact of using ML models on culture and organization? Who builds their models? We also used maturity , in other words how long had an enterprise organization been deploying ML models in production?

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’s harder for many folks in this role who don’t have any data or machine learning background and are thrown or thrust into shipping something like this. You really have to just go through it.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

JULY 9, 2019

Assessing whether a business stakeholder is trying to solve for a problem that is descriptive, predictive, or prescriptive and then re-framing the problem as supervised learning, unsupervised learning, or reinforcement learning, respectively. One of the ways I frame that is, “Are you looking to build a predictive model?

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

JANUARY 25, 2021

Models are neither code nor data, and we don’t have adequate tools for versioning models either (though tools like MLflow are a start). Frequent deployment assumes that the software can be built relatively quickly, but training a model can take days. AI, Machine Learning, and Data. Cloud computing is hybrid by nature.

Machine Learning 363

Machine Learning Software Testing Technology 363