Smart Data Collective

APRIL 5, 2022

Although SageMaker has become a popular hardware accelerator since it was launched in 2017, there are plenty of other overlooked hardware accelerators on the market. If you want to streamline various parts of the data science development process, then you should be aware of all of your options. Neptune.ai. Neptune.AI

Cost-Benefit 123

Cost-Benefit Machine Learning Data Science Unstructured Data 123

Domino Data Lab

MAY 8, 2019

Paco Nathan’s latest article features several emerging threads adjacent to model interpretability. 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. Adrian Weller (2017-07-29). “

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

datapine

NOVEMBER 19, 2019

Exciting and futuristic, the concept of computer vision is based on computing devices or programs gaining the ability to extract detailed information from visual images. Visual analytics: Around three million images are uploaded to social media every single day. billion in 2017 to $190.61 Artificial Intelligence (AI).

Technology 279

Technology Internet of Things IT IoT 279

Ontotext

MAY 18, 2020

Such problems and the complexities related to such computationally-intensive tasks are essential in the fields of weather forecasting, molecular modeling, airplane and spacecraft aerodynamics, personalized medicine, self-driving cars. There are four types of data sources that the team will work with.

Knowledge Discovery 72

Knowledge Discovery Experimentation Data-driven Metadata 72

Domino Data Lab

MAY 20, 2021

In this article we discuss why fitting models on imbalanced datasets is problematic, and how class imbalance is typically addressed. Insufficient training data in the minority class — In domains where data collection is expensive, a dataset containing 10,000 examples is typically considered to be fairly large.

Machine Learning 136

Machine Learning Metrics Data mining Knowledge Discovery 136

Domino Data Lab

FEBRUARY 4, 2019

People who attended JupyterCon 2017–2018 can attest, an “industry poster session” includes an open bar, catered hors d’oeuvres, lots of mingling … to paraphrase feedback from JupyterCon, “As a tech person, would I get up extra early to meet strangers for coffee at 8:00 am? I double-dare you not to visualize that cohort!

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

OCTOBER 23, 2019

This chapter will explore the numbers behind the numbers using ML and then creating an API to serve out the ML model. This means covering details like setting up your environment, deployment, and monitoring, in addition to creating models on clean data. Gathering the Data. Phrasing the Problem. questions to ask.

Statistics 59

Statistics Machine Learning Testing Modeling 59