Definitions

The data science and artificial intelligence
terms you need while reading the latest research

Convolutional Neural Networks

A convolutional neural network is a type of neural network that is most commonly applied to processing and analyzing visual imagery.

Neural Network Natural Language Processing Artificial Intelligence
05/17/2019 ∙ 1146 ∙ share

read it
Active Learning

Active learning is a form of semi-supervised machine learning where the algorithm chooses which data to learn from and queries a teacher for guidance.

Natural Language Processing Supervised Learning Machine Learning
05/17/2019 ∙ 1016 ∙ share

read it
Generative Adversarial Network

A generative adversarial network (GAN) is an unsupervised machine learning technique that trains two neural networks by forcing them to “outwit” each other.

Classifier Unsupervised Learning Machine Learning
05/17/2019 ∙ 996 ∙ share

read it
Bayes Theorem

Bayes’ theorem is a formula that governs how to assign a subjective degree of belief to a hypothesis and rationally update that probability with new evidence. Mathematically, it's the the likelihood of event B occurring given that A is true.

Machine Learning Posterior Probability Prior Probability
05/17/2019 ∙ 991 ∙ share

read it
Tensor

A Tensor is a mathematical object similar to, but more general than, a vector and often represented by an array of components that describe functions relevant to coordinates of a space. Put simply, a Tensor is an array of numbers that transform according to certain rules under a change of coordinates.

Neural Network Vector Machine Learning
05/17/2019 ∙ 888 ∙ share

read it
Natural Language Processing

In simple words, Natural Language Processing is a field which aims to make computer systems understand human speech. NLP is comprised of techniques to process, structure, categorize raw text and extract information.

05/17/2019 ∙ 887 ∙ share

read it
Evaluation Metrics

Evaluation metrics are used to measure the quality of the statistical or machine learning model.

Machine Learning Confusion Matrix
05/17/2019 ∙ 869 ∙ share

read it
Batch Normalization

Batch Normalization is a supervised learning technique that converts selected inputs in a neural network layer into a standard format, called normalizing.

Supervised Learning Deep Learning Loss Function
05/17/2019 ∙ 843 ∙ share

read it
Statistical Learning Theory

Statistical learning theory is the broad framework for studying the concept of inference in both supervised and unsupervised machine learning.

Probability Machine Learning Deep Learning
05/17/2019 ∙ 842 ∙ share

read it
Feature Extraction

Feature extraction is a process by which an initial set of data is reduced by identifying key features of the data for machine learning.

Machine Learning Natural Language Processing Unsupervised Learning
05/17/2019 ∙ 820 ∙ share

read it
Attention Models

Attention models break down complicated tasks into smaller areas of attention that are processed sequentially.

Vector Neural Network Computer Vision
05/17/2019 ∙ 798 ∙ share

read it

Drive top data science
& engineering talent
to openings at your organization