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

JUNE 18, 2019

The good news is that researchers from academia recently managed to leverage that large body of work and combine it with the power of scalable statistical inference for data cleaning. business and quality rules, policies, statistical signals in the data, etc.).

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

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. Above all, there needs to be a set methodology for data mining, collection, and structure within the organization before data is run through a deep learning algorithm or machine learning.

Big Data 106

Big Data Data Analytics Analytics Cost-Benefit 106

Cloudera

FEBRUARY 25, 2021

They require a deep enough knowledge of dozens of ML techniques in order to choose the right approach for a given use case, a thorough understanding of everything required to execute on that use case, as well as a solid foundation in statistics fundamentals to ensure their choices and implementations are mathematically sound and appropriate.

Deep Learning 81

Deep Learning Machine Learning Forecasting Visualization 81

Ontotext

DECEMBER 15, 2023

It includes only ML papers and related entities; this SPARQL query shows some statistics: papers tasks models datasets methods evaluations repos 376557 4267 24598 8322 2101 52519 153476 We can start with these repositories (most of them are on Github) and get all their topics. We can start with a connecting dataset like LinkedPapersWithCode.

Machine Learning 69

Machine Learning Interactive Deep Learning Statistics 69

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

O'Reilly on Data

JULY 28, 2020

This tradeoff between impact and development difficulty is particularly relevant for products based on deep learning: breakthroughs often lead to unique, defensible, and highly lucrative products, but investing in products with a high chance of failure is an obvious risk. Prototypes and Data Product MVPs.

Marketing 362

Marketing Experimentation Metrics Testing 362

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.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. Building an in-house team with AI, deep learning , machine learning (ML) and data science skills is a strategic move. The AGI research field is constantly evolving.

Cost-Benefit 88

Cost-Benefit Modeling Manufacturing Interactive 88

Smart Data Collective

JANUARY 13, 2022

It is obvious from the statistics that each customer, facing a bad customer service experience, does more than one step to hurt the business. For example, deep learning can be used to understand speech and also respond with speech. Think about your bad service experience with a brand and the actions you took after that.

Machine Learning 124

Machine Learning Cost-Benefit Marketing Deep Learning 124

Domino Data Lab

JULY 28, 2021

Data science is a field at the convergence of statistics, computer science and business. In this article, take a deep dive into data science and how Domino’s Enterprise MLOps platform allows you to scale data science in your business. In fact, deep learning was first described theoretically in 1943.

Data Science 45

Data Science Machine Learning Statistics Deep Learning 45

Sisense

APRIL 15, 2021

An ML user with statistics training and some basic knowledge of how deep learning neural networks operate can start with columns of revenue and annuity and generate additional features including mean, standard deviation, and kurtosis to enhance the model training data and thus improve prediction accuracy.

Analytics 98

Analytics Data Science Modeling Machine Learning 98

DataRobot Blog

MAY 26, 2022

These methods provided the benefit of being supported by rich literature on the relevant statistical tests to confirm the model’s validity—if a validator wanted to confirm that the input predictors of a regression model were indeed relevant to the response, they need only to construct a hypothesis test to validate the input.

Risk 52

Risk Modeling Metrics Business Objectives 52

FineReport

DECEMBER 19, 2019

When it comes to data analysis, from database operations, data cleaning, data visualization , to machine learning, batch processing, script writing, model optimization, and deep learning, all these functions can be implemented with Python, and different libraries are provided for you to choose. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

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. The drawback of GAMs is that they do not allow feature interactions. linear regression, trees) can be too rigid in their functional forms.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

Domino Data Lab

AUGUST 10, 2021

Some popular tool libraries and frameworks are: Scikit-Learn: used for machine learning and statistical modeling techniques including classification, regression, clustering and dimensionality reduction and predictive data analysis. PyTorch: used for deep learning models, like natural language processing and computer vision.

Modeling 64

Modeling IT Risk Management Deep Learning 64

IBM Big Data Hub

OCTOBER 12, 2023

Early iterations of the AI applications we interact with most today were built on traditional machine learning models. These models rely on learning algorithms that are developed and maintained by data scientists. Due to deep learning and other advancements, the field of AI remains in a constant and fast-paced state of flux.

Machine Learning 72

Machine Learning Deep Learning Interactive Modeling 72

Smarten

APRIL 19, 2024

Vendors, service providers and analytics solution companies will package data products and design data management solutions to support business users in their interaction with suppliers, customers and stakeholders, and to inform and support customers and end users as they choose products and options.

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

Domino Data Lab

JULY 22, 2019

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Cloudera

NOVEMBER 3, 2021

Thanks to pioneers like Andrew NG and Fei-Fei Li, GPUs have made headlines for performing particularly well with deep learning techniques. Today, deep learning and GPUs are practically synonymous. While deep learning is an excellent use of the processing power of a graphics card, it is not the only use.

Deep Learning 115

Deep Learning Machine Learning Data Science Cost-Benefit 115

IBM Big Data Hub

DECEMBER 20, 2023

ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. A semi-supervised learning model might use unsupervised learning to identify data clusters and then use supervised learning to label the clusters.

Machine Learning 79

Machine Learning Modeling Deep Learning Predictive Modeling 79

O'Reilly on Data

FEBRUARY 18, 2020

After several years of steady climbing—and after outstripping Java in 2017—Python-related interactions now comprise almost 10% of all usage. As statistics and related techniques become more important in software development, more programmers are encountering stats in programming classes.

Data-driven 335

Data-driven Software Statistics Marketing 335

datapine

NOVEMBER 19, 2019

AI refers to the autonomous intelligent behavior of software or machines that have a human-like ability to make decisions and to improve over time by learning from experience. Currently, popular approaches include statistical methods, computational intelligence, and traditional symbolic AI. Voice-as-User Interface (VUI).

Technology 279

Technology Internet of Things IT IoT 279

Smart Data Collective

OCTOBER 14, 2019

Ludwig is a tool that allows people to build data-based deep learning models to make predictions. It allows secure and interactive SQL analytics at the petabyte scale. Sometimes a data project is most effective if people can interact with the data. Here are some open-source options to consider.

Data Science 68

Data Science Machine Learning Big Data Interactive 68

Domino Data Lab

JULY 15, 2021

Originally created for software development, Python is used in a variety of contexts, including deep learning research and model deployment. RStudio is an IDE for the R language used primarily for statistical analysis as well as data visualization. Some common IDEs are RStudio and Jupyter Notebook.

Data Science 145

Data Science Visualization Modeling Enterprise 145

Domino Data Lab

MAY 8, 2019

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

JUNE 23, 2019

This talk will describe how you can navigate all these challenges that you’re going to face and build a business where every product interaction benefits from your investment in machine learning. It used deep learning to build an automated question answering system and a knowledge base based on that information.

Management 110

Management Machine Learning Experimentation Metrics 110

O'Reilly on Data

MAY 21, 2019

Consider deep learning, a specific form of machine learning that resurfaced in 2011/2012 due to record-setting models in speech and computer vision. Machine learning is not only appearing in more products and systems, but as we noted in a previous post , ML will also change how applications themselves get built in the future.

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238

Domino Data Lab

JULY 9, 2019

Here is a picture of The New York Times on its birthday in 1851, and for the vast majority of its lifespan this is pretty much what the user experience of interacting with The New York Times looks like. Editors can interact with this bot. You may wonder where would you put a data science group in The New York Times. We crowdsourced it.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

MARCH 24, 2020

HoloClean decouples the task of data cleaning into error detection (such as recognizing that the location “cicago” is erroneous) and repairing erroneous data (such as changing “cicago” to “Chicago”), and formalizes the fact that “data cleaning is a statistical learning and inference problem.”

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

O'Reilly on Data

JANUARY 25, 2021

We’ve explored usage across all publishing partners and learning modes, from live training courses and online events to interactive functionality provided by Katacoda and Jupyter notebooks. AI, Machine Learning, and Data. We’ve included search data in the graphs, although we have avoided using search data in our analysis.

Machine Learning 363

Machine Learning Software Testing Technology 363