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

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

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

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. Curiosities and anomalies in your training and testing data become genuine and sustained loss patterns. The drawback of GAMs is that they do not allow feature interactions.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

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

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

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 119

Deep Learning Machine Learning Data Science Cost-Benefit 119

Smart Data Collective

OCTOBER 14, 2019

Ludwig is a tool that allows people to build data-based deep learning models to make predictions. As long as two modules both conform to the same set of standards, you can swap them out, and due to the shared characteristics of the modules, this aspect of Kubernetes can shorten your integration testing process.

Data Science 69

Data Science Machine Learning Big Data Interactive 69

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. IDEs (also sometimes referred to as notebooks) is a coding tool that makes it easier to develop and test code. Some common IDEs are RStudio and Jupyter Notebook.

Data Science 145

Data Science Visualization Modeling Enterprise 145

Domino Data Lab

JUNE 23, 2019

Be aware that machine learning often involves working on something that isn’t guaranteed to work. As a result, Skomoroch advocates getting “designers and data scientists, machine learning folks together and using real data and prototyping and testing” as quickly as possible. Testing is critical. It is similar to R&D.

Management 110

Management Machine Learning Experimentation Metrics 110

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

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

A “data scientist” might build a multistage processing pipeline in Python, design a hypothesis test, perform a regression analysis over data samples with R, design and implement an algorithm in Hadoop, or communicate the results of our analyses to other members of the organization in a clear and concise fashion.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

MARCH 24, 2020

For example, a pre-existing correlation pulled from an organization’s database should be tested in a new experiment and not assumed to imply causation [3] , instead of this commonly encountered pattern in tech: A large fraction of users that do X do Z. HoloClean performs this automatically in a principled, statistical manner.

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. It’s particularly difficult if testing includes issues like fairness and bias. AI, Machine Learning, and Data.

Machine Learning 363

Machine Learning Software Testing Technology 363