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  • Akira AI Glossary

A

  • Average Pooling
  • Apache Hadoop
  • Adam Optimization
  • Automated Machine Learning
  • Apache Spark

B

  • Business Intelligence
  • Business Analytics
  • Bayesian Statistics
  • Bias Errors
  • Back Propagation Algorithm
  • Bootstrap Aggregation
  • Bidirectional Encoder Representations from Transformers

C

  • Convolution
  • Concordant-Discordant Ratio
  • Cross-Validation
  • Concave and Convex Function
  • Capsule Networks

D

  • Decision-Driven Data Analytics
  • Dropout Regularization
  • Dplyr
  • Deep Neural Networks
  • Data Mining
  • Dopamine
  • Deep Reinforcement Learning

E

  • ETL
  • Embeddings from Language Models
  • Edge computing in agriculture
  • Exploratory Data Analysis
  • Explainable Artificial Intelligence
  • End to End Machine Learning
  • Evaluation Metrics
  • ELI5
  • Edge Analytics

F

  • Few Shot Learning
  • Factor Analysis
  • Feature Hashing

G

  • Goodness of Fit
  • Gated Recurrent Unit
  • Gradient Descent
  • Google Duplex
  • Gradient Boosting
  • Generative Adversarial Networks

H

  • Hybrid Learning Models
  • Hyperparameter Tuning

L

  • Lightgbm
  • Lean and Augmented Data Learning

M

  • Meta-Learning

P

  • PyText
  • Probabilistic Programming

Q

  • Quantum Computing
  • Quantum Machine Learning

S

  • Stochastic Gradient Descent

T

  • Technological Convergence

U

  • Universal Language Model Fine-Tuning (ULMfit)

V

  • Vowpal Wabbit
  • Back to Glossary Page

Convolution

What is Convolution?

A mathematical operation which multiplies an input with a filter. Convolutions are the foundation of CNN, which excel at identifying edges and objects in images.

 

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