O'Reilly on Data

FEBRUARY 16, 2023

That’s what beta tests are for. Large language models like ChatGPT and Google’s LaMDA aren’t designed to give correct results. Remember that these tools aren’t doing math, they’re just doing statistics on a huge body of text. So it’s not surprising that things are wrong.

Testing 220

Testing Statistics Modeling Optimization 220

O'Reilly on Data

DECEMBER 12, 2019

Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML. Because all ML models make mistakes, everyone who cares about ML should also care about model debugging. [1]

Machine Learning 198

Machine Learning Modeling Testing Risk Management 198

O'Reilly on Data

DECEMBER 12, 2023

If the output of a model can’t be owned by a human, who (or what) is responsible if that output infringes existing copyright? In an article in The New Yorker , Jaron Lanier introduces the idea of data dignity, which implicitly distinguishes between training a model and generating output using a model.

Modeling 262

Modeling Software Sales Statistics 262

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. Among these, only statistical uncertainty has formal recognition.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

O'Reilly on Data

MARCH 26, 2024

TL;DR LLMs and other GenAI models can reproduce significant chunks of training data. Researchers are finding more and more ways to extract training data from ChatGPT and other models. And the space is moving quickly: SORA , OpenAI’s text-to-video model, is yet to be released and has already taken the world by storm.

Modeling 282

Modeling Machine Learning Risk Advertising 282

Domino Data Lab

JULY 1, 2021

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. The CRF model. What is Named Entity Recognition?

Modeling 111

Modeling Statistics Testing Metrics 111

Analytics Vidhya

APRIL 6, 2023

Introduction This article uses to predict student performance. For many applications, including online customer service, marketing, and finance, the stock price is a crucial challenge.

Finance 268

Finance Modeling Marketing Analytics 268

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. In this article, we turn our attention to the process itself: how do you bring a product to market? Identifying the problem.

Marketing 363

Marketing Experimentation Metrics Testing 363

IBM Big Data Hub

FEBRUARY 13, 2024

This data is fed into generational models, and there are a few to choose from, each developed to excel at a specific task. Generative adversarial networks (GANs) or variational autoencoders (VAEs) are used for images, videos, 3D models and music. Autoregressive models or large language models (LLMs) are used for text and language.

Enterprise 119

Enterprise Modeling Testing Data-driven 119

Smart Data Collective

OCTOBER 8, 2021

This article looks at how genetic algorithms (GA) and machine learning (ML) can help hedge fund organizations. Modern machine learning and back-testing; how quant hedge funds use it. Similarly, hedge funds often use modern machine learning and back-testing to analyze their quant models. Machine learning tests.

Machine Learning 114

Machine Learning Testing Strategy Modeling 114

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

O'Reilly on Data

MAY 18, 2020

This article answers these questions, based on our combined experience as both a lawyer and a data scientist responding to cybersecurity incidents, crafting legal frameworks to manage the risks of AI, and building sophisticated interpretable models to mitigate risk. Because statistics: Last is the inherently probabilistic nature of ML.

Risk 361

Risk Modeling Data Processing Software 361

The Unofficial Google Data Science Blog

SEPTEMBER 30, 2016

The statistics app for Google Sheets hopes to change that. Editor's note: We've mostly portrayed data science as statistical methods and analysis approaches based on big data. Steve's post is an excellent example of this, and we are thrilled to see him contribute this month's article. Make a copy from the menu.

Statistics 53

Statistics Big Data Data Science Marketing 53

Smarten

JUNE 29, 2018

This article focuses on the Independent Samples T Test technique of Hypothesis testing. What is the Independent Samples T Test Method of Hypothesis Testing? Let’s look at a sample of the Independent t-test on two variables. How Can the Independent Samples T Test Method Benefit an Organization?

Testing 40

Testing Statistics IT Business Intelligence 40

CIO Business Intelligence

JUNE 11, 2024

A 1958 Harvard Business Review article coined the term information technology, focusing their definition on rapidly processing large amounts of information, using statistical and mathematical methods in decision-making, and simulating higher order thinking through applications. What comes first: A new brand or operating model?

IT 110

IT Digital Transformation Broadcasting Business Objectives 110

Smarten

JUNE 29, 2018

This article discusses the Paired Sample T Test method of hypothesis testing and analysis. What is the Paired Sample T Test? The Paired Sample T Test is used to determine whether the mean of a dependent variable e.g., weight, anxiety level, salary, reaction time, etc., is the same in two related groups.

Business Analysis 40

Business Analysis Testing Statistics Advertising 40

Rocket-Powered Data Science

JULY 14, 2023

This was not a scientific or statistically robust survey, so the results are not necessarily reliable, but they are interesting and provocative. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.

Analytics 166

Analytics Internet of Things Prescriptive Analytics Data Science 166

Smarten

JUNE 26, 2018

This article describes chi square test of association and hypothesis testing. What is the Chi Square Test of Association Method of Hypothesis Testing? It is used to determine whether there is a statistically significant association between the two categorical variables. Use Case – 1.

Testing 40

Testing Statistics IT Business Intelligence 40

Andrew White

MARCH 28, 2019

an analytic or metric), or some other more sophisticated representative of a model or algorithm. There was an interesting article in today’s US print edition of the Financial Times. In tests every day, if something is found to be statistically significant then it must surely be. The problem is this is not necessarily so.

Data-driven 79

Data-driven Statistics Testing Metrics 79

Smarten

APRIL 19, 2024

In this article, we will discuss the current state of AI in analytics, as well as the future of this burgeoning industry and how it can be applied to analytics to simplify and clarify results and to make analytics easier for businesses and business users to leverage. GPT – Is based on a Large Language Model (LLM).

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Smarten

JUNE 25, 2018

This article provides a brief explanation of the definition and uses of the Descriptive Statistics algorithms. What is a Descriptive Statistics? Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data.

Statistics 40

Statistics Enterprise Measurement Business Intelligence 40

Perceptual Edge

APRIL 9, 2020

Statistics are playing a major role during the COVID-19 pandemic. The purpose of the Investor’s Business Daily article was to examine how the pandemic was affecting the stock market. Because the article did not identify its data source, I chose to base the graph below on official CDC data, so the numbers are a little different.

Testing 109

Testing Reporting Statistics Marketing 109

Domino Data Lab

JUNE 15, 2021

Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. anomaly detection).

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

Smart Data Collective

OCTOBER 8, 2023

This article delves into the profound impact data analytics can have on fast food legal cases. The Power of Data Analytics: An Overview Data analytics, in its simplest form, is the process of inspecting, cleansing, transforming, and modeling data to unearth useful information, draw conclusions, and support decision-making.

Data Analytics 94

Data Analytics Analytics Statistics Data Collection 94

The Unofficial Google Data Science Blog

JANUARY 16, 2018

by SANGHO YOON In this article, we discuss an approach to the design of experiments in a network. We present data from Google Cloud Platform (GCP) as an example of how we use A/B testing when users are connected. At Google, A/B testing plays a key role in better understanding our users and products.

Testing 58

Testing Experimentation Measurement Modeling 58

IBM Big Data Hub

JULY 6, 2023

Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. Ultimately, data science is used in defining new business problems that machine learning techniques and statistical analysis can then help solve.

Machine Learning 54

Machine Learning Data Science Statistics Deep Learning 54

Smarten

DECEMBER 5, 2022

In this article, we provide a list with links that will detail some of the many analytical techniques your business users will employ and provide examples of how these techniques can be used to solve problems and identify opportunities with clear, easy techniques and results. Paired Sample T Test. Chi Square Test.

Predictive Analytics 52

Predictive Analytics Predictive Modeling Forecasting Testing 52

datapine

JANUARY 6, 2022

For a more in-depth review of scales of measurement, read our article on data analysis questions. More often than not, it involves the use of statistical modeling such as standard deviation, mean and median. Standard deviation: this is another statistical term commonly appearing in quantitative analysis.

Visualization 246

Visualization Dashboards Cost-Benefit Measurement 246

Smarten

DECEMBER 3, 2023

This article will help you gather information about the topic of augmented analytics. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and AI model development, management and deployment.’ ‘You Maybe you just want to understand the analytics solution market better.

Key Performance Indicator 52

Key Performance Indicator Analytics IT Predictive Analytics 52

Cloudera

DECEMBER 3, 2021

For example, training an interview screening model using education data often contains gender information. As discussed in this article , model design can also be a source of bias too. Even something as simple as choosing a loss function can change the bias of a trained model. Model Drift. Regulation.

Uncertainty 102

Uncertainty Measurement Metrics Risk 102

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.

Machine Learning 244

Machine Learning Technology Deep Learning Data Science 244

FineReport

MARCH 24, 2024

This article explores the data analyst job description, covering essential skills, tools, education, certifications, and experience. Data analysts contribute value to organizations by uncovering trends, patterns, and insights through data gathering, cleaning, and statistical analysis.

Statistics 52

Statistics Data mining Visualization Reporting 52

Domino Data Lab

AUGUST 22, 2019

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

Smarten

MAY 31, 2022

When combined with Citizen Data Scientist initiatives, the adoption and use of predictive modeling and forecasting techniques can be a boon to any enterprise. Team members who have access to augmented analytics and assisted predictive modeling can plan better, predict more accurately and dependably meet goals and objectives.

Predictive Analytics 67

Predictive Analytics Analytics Predictive Modeling Prescriptive Analytics 67

Domino Data Lab

APRIL 21, 2021

In this article, we’ll discuss the challenge organizations face around fraud detection, how machine learning can be used to identify and spot anomalies that the human eye might not catch. In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data.

Statistics 69

Statistics Machine Learning Modeling Metrics 69

datapine

NOVEMBER 27, 2019

We detailed the benefits and costs of good or bad quality data in our previous article on data quality management , where you can read the five important pillars to follow. Another increasing factor in the future of business intelligence is testing AI in a duel.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

Insight

MARCH 12, 2020

The AIgent was built with BERT, Google’s state-of-the-art language model. In this article, I will discuss the construction of the AIgent, from data collection to model assembly. The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language.

Modeling 59

Modeling Metadata Publishing Sales 59