Smart Data Collective

JUNE 4, 2021

Data science needs knowledge from a variety of fields including statistics, mathematics, programming, and transforming data. Mathematics, statistics, and programming are pillars of data science. In data science, use linear algebra for understanding the statistical graphs. It is the building block of statistics.

Data Science 105

Data Science Statistics Deep Learning Machine Learning 105

datapine

MAY 14, 2019

This interdisciplinary field of scientific methods, processes, and systems helps people extract knowledge or insights from data in a host of forms, either structured or unstructured, similar to data mining. 2) “Deep Learning” by Ian Goodfellow, Yoshua Bengio and Aaron Courville. click for book source**.

Data Science 350

Data Science Machine Learning Data-driven Big Data 350

Data Science 101

DECEMBER 11, 2019

Every year they host an excellent and influential conference focusing on many areas of data science. Topics of interest include artificial intelligence, big data, data analytics, data science, data mining, deep learning, knowledge graphs, machine learning, relational databases and statistical methods.

KDD 81

KDD Knowledge Discovery Data mining Data Science 81

O'Reilly on Data

MARCH 31, 2020

If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML). But there’s a host of new challenges when it comes to managing AI projects: more unknowns, non-deterministic outcomes, new infrastructures, new processes and new tools.

Management 339

Management Machine Learning Experimentation Metrics 339

DataRobot Blog

MAY 26, 2022

Validating Modern Machine Learning (ML) Methods Prior to Productionization. Validating Machine Learning Models. Last time , we discussed the steps that a modeler must pay attention to when building out ML models to be utilized within the financial institution.

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. The majority (72%) of enterprises that use APIs for model access use models hosted on their cloud service providers.

Cost-Benefit 89

Cost-Benefit Modeling Manufacturing Interactive 89

Domino Data Lab

MAY 8, 2019

I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. Machine learning model interpretability. Other good related papers include: “ Towards A Rigorous Science of Interpretable Machine Learning ”. Not yet, if ever.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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

JULY 9, 2019

Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. I still believe that data science is the craft of trying to apply machine learning to some real world problem.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

O'Reilly on Data

JANUARY 25, 2021

We’re not pretending the frameworks themselves are comparable—Spring is primarily for backend and middleware development (though it includes a web framework); React and Angular are for frontend development; and scikit-learn and PyTorch are machine learning libraries. AI, Machine Learning, and Data.

Machine Learning 363

Machine Learning Software Testing Technology 363