Natural Language Processing
Machine Learning
Computer Vision
∙
05/17/2019
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.
Classifier
Estimator (Statistics)
Autoencoder
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07/22/2020
Generative Adversarial Network
A generative adversarial network (GAN) is an unsupervised machine learning architecture that trains two neural networks by forcing them to “outwit” each other.
Machine Learning
Classifier
Harmonic Mean
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05/17/2019
Evaluation Metrics
Evaluation metrics are used to measure the quality of the statistical or machine learning model.
ImageNet
Classifier
Estimator (Statistics)
∙
05/17/2019
Convolutional Neural Network
A convolutional neural network, or CNN, is a deep learning neural network designed for processing structured arrays of data such as images.
Activation Function
Variance
Deep Learning
∙
05/17/2019
Batch Normalization
Batch Normalization is a supervised learning technique that converts selected inputs in a neural network layer into a standard format, called normalizing.
Computer Vision
Neural Network
Natural Language Processing
∙
05/17/2019
Attention Models
Attention models break down complicated tasks into smaller areas of attention that are processed sequentially.
Machine Learning
Odds (Probability)
Prior Probability
∙
05/17/2019
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
Bayesian Inference
Bayes Theorem
∙
05/17/2019
Posterior Probability
In statistics, the posterior probability expresses how likely a hypothesis is given a particular set of data.
Classifier
Machine Learning
Harmonic Mean
∙
05/17/2019
F-Score
The F score, also called the F1 score or F measure, is a measure of a test’s accuracy.
Supervised Learning
Unsupervised Learning
Classifier
∙
05/17/2019
Deep Belief Network
Deep Belief Networks (DBNs) are a laddering of individual unsupervised networks that use each network’s hidden layer as the input for the next layer.
Vector
Classifier
Open Source
∙
05/17/2019