SVD
Last updated
, generated by Sumble
Explore more → **SVD**
What is SVD?
Singular Value Decomposition (SVD) is a matrix factorization method that decomposes a matrix into three other matrices: U, Σ, and V^T. It is widely used in various applications like dimensionality reduction, image compression, recommendation systems, and solving linear least squares problems. SVD extracts the most important features from a matrix, allowing for efficient representation and analysis of data.
What other technologies are related to SVD?
SVD Competitor Technologies
SVD Complementary Technologies
scikit-learn
Scikit-learn is a machine learning library that provides implementations of SVD and other related algorithms (like PCA), making it a complementary tool.
TensorFlow
TensorFlow is a deep learning framework that can be used to implement SVD or leverage SVD as part of more complex models, making it a complementary technology.
PyTorch
PyTorch is another deep learning framework that can be used to implement SVD or leverage SVD as part of more complex models, making it a complementary technology.
Number of organizations that mention technology
ⓘ Tap on a tech to explore matching organizations
Which job functions commonly mention SVD?
AI Engineer
35 AI Engineer jobs mention SVD
View 35
jobs on Sumble
Machine Learning
20 Machine Learning jobs mention SVD
View 20
jobs on Sumble
Data Scientist
46 Data Scientist jobs mention SVD
View 46
jobs on Sumble
Data, Analytics & Machine Learning
115 Data, Analytics & Machine Learning jobs mention SVD
View 115
jobs on Sumble
Software Architect
5 Software Architect jobs mention SVD
View 5
jobs on Sumble
Which organizations are mentioning SVD?
Summary powered by 
Sumble
Sumble Find the right accounts, contact, message, and time to sell
Whether you're looking to get your foot in the door, find the right person to talk to, or close the deal — accurate, detailed, trustworthy, and timely information about the organization you're selling to is invaluable.
Use Sumble to: