From 1d2f7eb6785f6eb2d0e2c1253ecd373204b31943 Mon Sep 17 00:00:00 2001 From: Sven van der Burg Date: Tue, 5 Sep 2023 10:35:59 +0200 Subject: [PATCH] Fix deep learning terms --- glossary.yml | 65 ++++++++++++++++++++++++++++++---------------------- 1 file changed, 37 insertions(+), 28 deletions(-) diff --git a/glossary.yml b/glossary.yml index fcca6b9c..9c7fb77b 100644 --- a/glossary.yml +++ b/glossary.yml @@ -10139,11 +10139,12 @@ - perceptron - neural_network - machine_learning - term: "convolutional neural network (cnn)" - def: > - A class of artificial neural network that is primarily used to analyze images. A CNN has layers - that perform convolutions, where a filter is shifted over the data, instead of the general - matrix multiplications that we see in fully connected neural network layers. + en: + term: "convolutional neural network (cnn)" + def: > + A class of artificial neural network that is primarily used to analyze images. A CNN has layers + that perform convolutions, where a filter is shifted over the data, instead of the general + matrix multiplications that we see in fully connected neural network layers. - slug: rnn ref: @@ -10152,11 +10153,12 @@ - perceptron - neural_network - machine_learning - term: recurrent neural network - def: > - A class of [artificial neural networks](#neural_network) where connections between nodes can - create a cycle. This allows the network to exhibit behavior that is dynamic over time. This - type of network is applicable to tasks like speech and handwriting recognition. + en: + term: recurrent neural network + def: > + A class of [artificial neural networks](#neural_network) where connections between nodes can + create a cycle. This allows the network to exhibit behavior that is dynamic over time. This + type of network is applicable to tasks like speech and handwriting recognition. - slug: epoch_dl ref: @@ -10165,11 +10167,12 @@ - perceptron - neural_network - machine_learning - term: epoch (deep learning) - def: > - In [deep learning](#deep_learning), an epoch is one cycle in the deep learning process where all - the training data has been fed to the algorithm once. Training a deep neural networks usually - consists of multiple epochs. + en: + term: epoch (deep learning) + def: > + In [deep learning](#deep_learning), an epoch is one cycle in the deep learning process where all + the training data has been fed to the algorithm once. Training a deep neural networks usually + consists of multiple epochs. - slug: learning_rate ref: @@ -10178,19 +10181,23 @@ - perceptron - neural_network - machine_learning - def: > - In [artificial neural networks](#neural_network), the learning rate is a hyper-parameter that - determines the pace at which the network adjusts the weights to move down the loss gradient. - A large learning rate can speed up training, but the network might overshoot and miss the - minimum. A small learning rate will overshoot less, but will be slower. It can also get more - easily stuck in local minima. + en: + term: learning rate (deep learning) + def: > + In [artificial neural networks](#neural_network), the learning rate is a hyper-parameter that + determines the pace at which the network adjusts the weights to move down the loss gradient. + A large learning rate can speed up training, but the network might overshoot and miss the + minimum. A small learning rate will overshoot less, but will be slower. It can also get more + easily stuck in local minima. - slug: class_imbalance ref: - machine_learning - def: > - Class imbalance refers to the problem in [machine learning](machine_learning) where there is an - unequal distribution of classes in the dataset. + en: + term: class imbalance + def: > + Class imbalance refers to the problem in [machine learning](machine_learning) where there is an + unequal distribution of classes in the dataset. - slug: hidden_layer ref: @@ -10198,7 +10205,9 @@ - machine_learning - deep_learning - perceptron - def: > - A hidden layer in a [neural network](#neural_network) refers to the layers of neurons that are - not directly connected to input or output. The layers are "hidden" because you do not directly - observe their input and output values. + en: + term: hidden layer (deep learning) + def: > + A hidden layer in a [neural network](#neural_network) refers to the layers of neurons that are + not directly connected to input or output. The layers are "hidden" because you do not directly + observe their input and output values.