Birst BI

NOVEMBER 20, 2018

When you hear about Data Science, Big Data, Analytics, Artificial Intelligence, Machine Learning, or Deep Learning, you may end up feeling a bit confused about what these terms mean. A foundational data analysis tool is Statistics , and everyone intuitively applies it daily. So, what do these terms really mean?

Statistics 84

Statistics Machine Learning Deep Learning Optimization 84

IBM Big Data Hub

APRIL 22, 2024

Generative AI refers to deep-learning models that can take raw data and “learn” to generate statistically probable outputs when prompted. Transforming challenges into solutions The post AI this Earth Day: Top opportunities to advance sustainability initiatives appeared first on IBM Blog.

IoT 86

IoT Internet of Things Cost-Benefit Reporting 86

IBM Big Data Hub

NOVEMBER 29, 2023

Furthermore, as modeling techniques become increasingly sophisticated in data science, including deep learning and predictive and generative models, companies and vendors must work diligently to prevent unintentional connections that could leak a person’s identity and expose them to third-party attacks.

Metrics 89

Metrics Machine Learning Risk Statistics 89

datapine

MAY 14, 2019

2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. Best for: This best data science book is especially effective for those looking to enter the data-driven machine learning and deep learning avenues of the field. “Machine Learning Yearning” by Andrew Ng.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

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

IBM Big Data Hub

DECEMBER 19, 2023

In this blog we’ll go over how machine learning techniques, powered by artificial intelligence, are leveraged to detect anomalous behavior through three different anomaly detection methods: supervised anomaly detection, unsupervised anomaly detection and semi-supervised anomaly detection.

Machine Learning 72

Machine Learning Optimization Unstructured Data Sales 72

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.” Generative AI uses advanced machine learning algorithms and techniques to analyze patterns and build statistical models.

Enterprise 125

Enterprise Modeling Testing Data-driven 125

Sisense

APRIL 10, 2020

R is a tool built by statisticians mainly for mathematics, statistics, research, and data analysis. Here, we will implement the XG-Boost algorithm, an algorithm that learns on the basis of training data (which we loaded earlier in both R and Python programming languages) with the help of probability and statistics.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

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

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

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects.

Data Science 58

Data Science Machine Learning Cost-Benefit Deep Learning 58

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

Jedox

JULY 31, 2018

Part one of our blog series explored how people are the driving force behind the digital transformation and how it is fueled by artificial intelligence and machine learning. It runs statistics and algorithms (also known as data mining) on masses of historical data to calculate probabilities and future events.

Technology 43

Technology Prescriptive Analytics Machine Learning Business Intelligence 43

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

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

bridgei2i

OCTOBER 7, 2020

To get a perspective on evolving trends in insurance in the post-COVID world, read BRIDGEi2i’s blog here. Underwriting essentially means evaluating the risk proposition of an insurance cover and determining how much an insurer should charge for the same by studying historical data and statistical models. click here.

Insurance 52

Insurance Sales Strategy Digital Transformation 52

Domino Data Lab

JULY 17, 2021

While more advanced models for speaker verification exist, this blog will form a basis of speech signal processing. Further, deep learning methods are built on the foundation of signal processing. NOTE : The scope of this blog is a machine learning approach to TISV. The same code can be used for more speakers.

Modeling 64

Modeling Testing Machine Learning Measurement 64

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

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.

Cost-Benefit 96

Cost-Benefit Modeling Manufacturing Interactive 96

Domino Data Lab

AUGUST 9, 2021

Before selecting a tool, you should first know your end goal – machine learning or deep learning. Machine learning identifies patterns in data using algorithms that are primarily based on traditional methods of statistical learning. Deep learning is sometimes considered a subset of machine learning.

Modeling 111

Modeling Deep Learning Machine Learning Statistics 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. This blog post motivates this problem more fully, and discusses monotonic splines and lattices as a solution.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

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

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

Domino Data Lab

APRIL 21, 2021

In contrast, the decision tree classifies observations based on attribute splits learned from the statistical properties of the training data. Machine Learning-based detection – using statistical learning is another approach that is gaining popularity, mostly because it is less laborious. describe().

Statistics 69

Statistics Machine Learning Modeling Metrics 69

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 82

Machine Learning Modeling Deep Learning Predictive Modeling 82

Domino Data Lab

JULY 22, 2019

This blog post provides a concise session summary, a video, and a written transcript. 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. Session Summary. Tukey did this paper.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

DECEMBER 17, 2019

This blog post provides high-level insights into GANs. and webinar provide more depth than this blog post as they cover an implementation of a basic GAN model and demonstrate how adversarial networks can be used to generate training samples. Semi-supervised learning is one strategy for reducing the number of labels.

Deep Learning 64

Deep Learning Machine Learning Modeling Statistics 64

Smarten

APRIL 19, 2024

The Current State of AI in Analytics By combining machine learning with AI, analytics vendors can and have made strides in providing user-friendly, self-serve augmented analytics products that include the newest versions of these technologies: Machine Learning (ML) Generative Artificial Intelligence (GenAI) Deep Learning Natural Language Processing (..)

Analytics 57

Analytics Predictive Modeling KPI Machine Learning 57

IBM Big Data Hub

OCTOBER 12, 2023

Unlike basic machine learning models, deep learning models allow AI applications to learn how to perform new tasks that need human intelligence, engage in new behaviors and make decisions without human intervention. Explore watsonx.ai

Machine Learning 74

Machine Learning Deep Learning Interactive Modeling 74

DataRobot

NOVEMBER 9, 2021

Signal classification models are typically built using time series principles; traditionally used features that include central, windowed, lag, and lead statistics can do the job but sometimes there might be scenarios where we want to eke out more performance out of the data. Image courtesy towardsAI. See DataRobot in Action. Request a Demo.

Visualization 52

Visualization Unstructured Data Statistics Metrics 52

O'Reilly on Data

MARCH 20, 2019

Watermarking is a term borrowed from the deep learning security literature that often refers to putting special pixels into an image to trigger a desired outcome from your model. A lot of the contemporary academic machine learning security literature focuses on adaptive learning, deep learning, and encryption.

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

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. Contact us today.

Data Science 145

Data Science Visualization Modeling Enterprise 145

Smart Data Collective

OCTOBER 14, 2019

Ludwig is a tool that allows people to build data-based deep learning models to make predictions. When Google talked about releasing this tool in its blog, the brand pointed out that if you don’t protect user data, you risk losing people’s trust. Here are some open-source options to consider. Kubernetes.

Data Science 67

Data Science Machine Learning Big Data Interactive 67

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.

Technology 279

Technology Internet of Things IT IoT 279

Domino Data Lab

MAY 8, 2019

Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Finale Doshi-Velez, Been Kim (2017-02-28) ; see also the Domino blog article about TCAV. 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

Smart Data Collective

NOVEMBER 9, 2022

Companies that work on machine learning for health care, like Google, create large groups of medical images selected by physicians. Machine learning algorithms use these sets of visual data to look for statistical patterns to identify which image features allow you to assume that it is worthy of a particular label or diagnosis.

Machine Learning 96

Machine Learning Testing Cost-Benefit Data-driven 96

O'Reilly on Data

MARCH 24, 2020

By Wansink’s own admission in the blog post, that’s not what happened in his lab.” So when you’re missing data or have “low-quality data,” you use assumptions, statistics, and inference to repair your data. HoloClean performs this automatically in a principled, statistical manner.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346