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

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

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

O'Reilly on Data

APRIL 19, 2021

First, 82% of the respondents are using supervised learning, and 67% are using deep learning. Deep learning is a set of algorithms that are common to almost all AI approaches, so this overlap isn’t surprising. 58% claimed to be using unsupervised learning. Risks checked for during development.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

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 118

Enterprise Modeling Testing Data-driven 118

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 104

Big Data Data Analytics Analytics Cost-Benefit 104

IBM Big Data Hub

APRIL 22, 2024

The US City of Atlanta , for example, uses IBM data collection, machine learning and AI to monitor public transit tunnel ventilation systems and predict potential failures that could put passengers at risk.

IoT 78

IoT Internet of Things Cost-Benefit Reporting 78

O'Reilly on Data

JULY 28, 2020

This isn’t always simple, since it doesn’t just take into account technical risk; it also has to account for social risk and reputational damage. A product needs to balance the investment of resources against the risks of moving forward without a full understanding of the data landscape. arbitrary stemming, stop word removal.).

Marketing 362

Marketing Experimentation Metrics Testing 362

O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. That’s where model debugging comes in. Sensitivity analysis.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

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 53

Data Science Machine Learning Cost-Benefit Deep Learning 53

bridgei2i

OCTOBER 7, 2020

The unprecedented circumstances surrounding pandemic have increased awareness around insurable risks across different financial losses that can be covered as everyone in the world have faced financial losses like unemployment, travel cancellation, health issues, business interruptions, etc. The way ahead for insurers.

Insurance 52

Insurance Sales Strategy Digital Transformation 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 90

Cost-Benefit Modeling Manufacturing Interactive 90

O'Reilly on Data

MARCH 31, 2020

Pragmatically, machine learning is the part of AI that “works”: algorithms and techniques that you can implement now in real products. We won’t go into the mathematics or engineering of modern machine learning here. Machine learning adds uncertainty. Managing Machine Learning Projects” (AWS).

Management 333

Management Machine Learning Experimentation Metrics 333

O'Reilly on Data

MARCH 18, 2020

Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. Managing AI/ML risk. We asked respondents to select all of the applicable risks they try to control for in building and deploying ML models.

Enterprise 310

Enterprise Deep Learning Data Governance Risk 310

Domino Data Lab

AUGUST 10, 2021

Models can predict things before they happen more accurately than humans, such as catastrophic weather events or who is at risk of imminent death in a hospital. PyTorch: used for deep learning models, like natural language processing and computer vision. It’s used for developing deep learning models.

Modeling 64

Modeling IT Risk Management Deep Learning 64

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

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. More knots make the learned feature transformation smoother and more capable of approximating any monotonic function.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

O'Reilly on Data

FEBRUARY 18, 2020

There’s plenty of security risks for business executives, sysadmins, DBAs, developers, etc., Normalized search frequency of top terms on the O’Reilly online learning platform in 2019 (left) and the rate of change for each term (right). to be wary of. Figure 1 (above).

Data-driven 336

Data-driven Software Statistics Marketing 336

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. Watermark attacks. Newer types of fair and private models (e.g.,

Modeling 222

Modeling Machine Learning Predictive Modeling Consulting 222

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. Supervised learning is commonly used for risk assessment, image recognition, predictive analytics and fraud detection, and comprises several types of algorithms.

Machine Learning 74

Machine Learning Modeling Deep Learning Predictive Modeling 74

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. 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

MARCH 3, 2019

O’Reilly Media had an earlier survey about deep learning tools which showed the top three frameworks to be TensorFlow (61% of all respondents), Keras (25%), and PyTorch (20%)—and note that Keras in this case is likely used as an abstraction layer atop TensorFlow. The data types used in deep learning are interesting.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

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

Domino Data Lab

MAY 8, 2019

That’s a risk in case, say, legislators – who don’t understand the nuances of machine learning – attempt to define a single meaning of the word interpret. 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.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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

JUNE 23, 2019

It used deep learning to build an automated question answering system and a knowledge base based on that information. This really rewards companies with an experimental culture where they can take intelligent risks and they’re comfortable with those uncertainties.

Management 110

Management Machine Learning Experimentation Metrics 110

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

Domino Data Lab

JULY 9, 2019

You can sleep at night as a data scientician and you know you’re not building a random number generator, but the people from product, they don’t want to know just that you can predict who’s going to be at risk. They want to know what are the risky behaviors. ” But then I realized I could crowdsource it.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

MARCH 24, 2020

John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing. So when you’re missing data or have “low-quality data,” you use assumptions, statistics, and inference to repair your data.

Machine Learning 347

Machine Learning Statistics Data Quality Modeling 347

O'Reilly on Data

JANUARY 25, 2021

Observability” risks becoming the new name for monitoring. AI, Machine Learning, and Data. Healthy growth in artificial intelligence has continued: machine learning is up 14%, while AI is up 64%; data science is up 16%, and statistics is up 47%. And that’s unfortunate.

Machine Learning 363

Machine Learning Software Testing Technology 363