Machine Learning Glossary: Sequence Models

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analytics-action-close="Callout Profile dismissed" analytics-label="Create Collection Callout" class="devsite-page-bookmark-tooltip nocontent" dismiss-button="true" id="devsite-collections-dropdown" dismiss-button-text="Dismiss" close-button-text="Got it"> <devsite-bookmark></devsite-bookmark> <span slot="popout-heading"> Stay organized with collections </span> <span slot="popout-contents"> Save and categorize content based on your preferences. </span> </devsite-feature-tooltip> <div class="devsite-page-title-meta"><devsite-view-release-notes></devsite-view-release-notes></div> <devsite-toc class="devsite-nav" depth="2" devsite-toc-embedded > </devsite-toc> <div class="devsite-article-body clearfix "> <p> <devsite-mathjax config="TeX-AMS-MML_SVG"></devsite-mathjax></p> <p>This page contains Sequence Models glossary terms. For all glossary terms, <a href="/machine-learning/glossary">click here</a>.</p> <p><a class="glossary-anchor" name="b"></a> <h2 class="glossary" id="b" data-text="B" tabindex="-1">B</h2></p> <p><a class="glossary-anchor" name="bigram"></a> <h2 class="hide-from-toc" id="bigram" data-text=" bigram" tabindex="-1"> bigram</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> <div class="glossary-icon" title="Language Evaluation">#language</div> </div></p> <p>An <a href="#N-gram"><strong>N-gram</strong></a> in which N=2.</p> <p><a class="glossary-anchor" name="e"></a> <h2 class="glossary" id="e" data-text="E" tabindex="-1">E</h2></p> <p><a class="glossary-anchor" name="exploding_gradient_problem"></a> <h2 class="hide-from-toc" id="exploding-gradient-problem" data-text=" exploding gradient problem" tabindex="-1"> exploding gradient problem</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> </div></p> <p>The tendency for <a href="/machine-learning/glossary#gradient"><strong>gradients</strong></a> in <a href="/machine-learning/glossary#deep_neural_network"><strong>deep neural networks</strong></a> (especially <a href="#recurrent_neural_network"><strong>recurrent neural networks</strong></a>) to become surprisingly steep (high). Steep gradients often cause very large updates to the <a href="/machine-learning/glossary#weight"><strong>weights</strong></a> of each <a href="/machine-learning/glossary#node"><strong>node</strong></a> in a deep neural network.</p> <p>Models suffering from the exploding gradient problem become difficult or impossible to train. <a href="#gradient_clipping"><strong>Gradient clipping</strong></a> can mitigate this problem.</p> <p>Compare to <a href="#vanishing_gradient_problem"><strong>vanishing gradient problem</strong></a>.</p> <p><a class="glossary-anchor" name="f"></a> <h2 class="glossary" id="f" data-text="F" tabindex="-1">F</h2></p> <p><a class="glossary-anchor" name="forget_gate"></a> <h2 class="hide-from-toc" id="forget-gate" data-text="forget gate" tabindex="-1">forget gate</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> </div></p> <p>The portion of a <a href="#Long_Short-Term_Memory"><strong>Long Short-Term Memory</strong></a> cell that regulates the flow of information through the cell. Forget gates maintain context by deciding which information to discard from the cell state.</p> <p><a class="glossary-anchor" name="g"></a> <h2 class="glossary" id="g" data-text="G" tabindex="-1">G</h2></p> <p><a class="glossary-anchor" name="gradient_clipping"></a> <h2 class="hide-from-toc" id="gradient-clipping" data-text=" gradient clipping" tabindex="-1"> gradient clipping</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> </div></p> <p>A commonly used mechanism to mitigate the <a href="#exploding_gradient_problem"><strong>exploding gradient problem</strong></a> by artificially limiting (clipping) the maximum value of gradients when using <a href="/machine-learning/glossary#gradient_descent"><strong>gradient descent</strong></a> to <a href="/machine-learning/glossary#training"><strong>train</strong></a> a model.</p> <p><a class="glossary-anchor" name="l"></a> <h2 class="glossary" id="l" data-text="L" tabindex="-1">L</h2></p> <p><a class="glossary-anchor" name="Long_Short-Term_Memory"></a> <h2 class="hide-from-toc" id="long-short-term-memory-lstm" data-text=" Long Short-Term Memory (LSTM)" tabindex="-1"> Long Short-Term Memory (LSTM)</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> </div></p> <p>A type of cell in a <a href="#recurrent_neural_network"><strong>recurrent neural network</strong></a> used to process sequences of data in applications such as handwriting recognition, machine translation, and image captioning. LSTMs address the <a href="#vanishing_gradient_problem"><strong>vanishing gradient problem</strong></a> that occurs when training RNNs due to long data sequences by maintaining history in an internal memory state based on new input and context from previous cells in the RNN.</p> <p><a class="glossary-anchor" name="LSTM"></a> <h2 class="hide-from-toc" id="lstm" data-text=" LSTM" tabindex="-1"> LSTM</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> </div></p> <p>Abbreviation for <a href="#Long_Short-Term_Memory"><strong>Long Short-Term Memory</strong></a>.</p> <p><a class="glossary-anchor" name="n"></a> <h2 class="glossary" id="n" data-text="N" tabindex="-1">N</h2></p> <p><a class="glossary-anchor" name="N-gram"></a> <h2 class="hide-from-toc" id="n-gram" data-text=" N-gram" tabindex="-1"> N-gram</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> <div class="glossary-icon" title="Language Evaluation">#language</div> </div></p> <p>An ordered sequence of N words. For example, <em>truly madly</em> is a 2-gram. Because order is relevant, <em>madly truly</em> is a different 2-gram than <em>truly madly</em>.</p> <table> <tr> <th>N</th> <th>Name(s) for this kind of N-gram</th> <th>Examples</th> </tr> <tr> <td>2 </td> <td>bigram or 2-gram </td> <td><em>to go, go to, eat lunch, eat dinner</em> </td> </tr> <tr> <td>3 </td> <td>trigram or 3-gram </td> <td><em>ate too much, three blind mice, the bell tolls</em> </td> </tr> <tr> <td>4 </td> <td>4-gram </td> <td><em>walk in the park, dust in the wind, the boy ate lentils</em> </td> </tr> </table> <p>Many <a href="/machine-learning/glossary#natural_language_understanding"><strong>natural language understanding</strong></a> models rely on N-grams to predict the next word that the user will type or say. For example, suppose a user typed <em>three blind</em>. An NLU model based on trigrams would likely predict that the user will next type <em>mice</em>.</p> <p>Contrast N-grams with <a href="/machine-learning/glossary#bag_of_words"><strong>bag of words</strong></a>, which are unordered sets of words.</p> <p><a class="glossary-anchor" name="r"></a> <h2 class="glossary" id="r" data-text="R" tabindex="-1">R</h2></p> <p><a class="glossary-anchor" name="recurrent_neural_network"></a> <h2 class="hide-from-toc" id="recurrent-neural-network" data-text=" recurrent neural network " tabindex="-1"> recurrent neural network </h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> </div></p> <p>A <a href="/machine-learning/glossary#neural_network"><strong>neural network</strong></a> that is intentionally run multiple times, where parts of each run feed into the next run. Specifically, hidden layers from the previous run provide part of the input to the same hidden layer in the next run. Recurrent neural networks are particularly useful for evaluating sequences, so that the hidden layers can learn from previous runs of the neural network on earlier parts of the sequence.</p> <p>For example, the following figure shows a recurrent neural network that runs four times. Notice that the values learned in the hidden layers from the first run become part of the input to the same hidden layers in the second run. Similarly, the values learned in the hidden layer on the second run become part of the input to the same hidden layer in the third run. In this way, the recurrent neural network gradually trains and predicts the meaning of the entire sequence rather than just the meaning of individual words.</p> <p> <img src="/static/machine-learning/glossary/images/RNN.svg" width="545" loading="lazy" alt="An RNN that runs four times to process four input words." > </p> <p><a class="glossary-anchor" name="RNN"></a> <h2 class="hide-from-toc" id="rnn" data-text=" RNN" tabindex="-1"> RNN</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> </div></p> <p>Abbreviation for <a href="#recurrent_neural_network"><strong>recurrent neural networks</strong></a>.</p> <p><a class="glossary-anchor" name="s"></a> <h2 class="glossary" id="s" data-text="S" tabindex="-1">S</h2></p> <p><a class="glossary-anchor" name="sequence_model"></a> <h2 class="hide-from-toc" id="sequence-model" data-text=" sequence model" tabindex="-1"> sequence model</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> </div></p> <p>A model whose inputs have a sequential dependence. For example, predicting the next video watched from a sequence of previously watched videos.</p> <p><a class="glossary-anchor" name="t"></a> <h2 class="glossary" id="t" data-text="T" tabindex="-1">T</h2></p> <p><a class="glossary-anchor" name="timestep"></a> <h2 class="hide-from-toc" id="timestep" data-text="timestep" tabindex="-1">timestep</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> </div></p> <p>One "unrolled" cell within a <a href="#recurrent_neural_network"><strong>recurrent neural network</strong></a>. For example, the following figure shows three timesteps (labeled with the subscripts t-1, t, and t+1):</p> <p> <img src="/static/machine-learning/glossary/images/Simple_RNN.svg" loading="lazy" alt="Three timesteps in a recurrent neural network. The output of the first timestep becomes input to the second timestep. The output of the second timestep becomes input to the third timestep." > </p> <p><a class="glossary-anchor" name="trigram"></a> <h2 class="hide-from-toc" id="trigram" data-text=" trigram" tabindex="-1"> trigram</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> <div class="glossary-icon" title="Language Evaluation">#language</div> </div></p> <p>An <a href="#N-gram"><strong>N-gram</strong></a> in which N=3.</p> <p><a class="glossary-anchor" name="v"></a> <h2 class="glossary" id="v" data-text="V" tabindex="-1">V</h2></p> <p><a class="glossary-anchor" name="vanishing_gradient_problem"></a> <h2 class="hide-from-toc" id="vanishing-gradient-problem" data-text="vanishing gradient problem" tabindex="-1">vanishing gradient problem</h2> <div class="glossary-icon-container"> <div class="glossary-icon" title="Sequence Models">#seq</div> </div></p> <p>The tendency for the gradients of early <a href="/machine-learning/glossary#hidden_layer"><strong>hidden layers</strong></a> of some <a href="/machine-learning/glossary#deep_neural_network"><strong>deep neural networks</strong></a> to become surprisingly flat (low). Increasingly lower gradients result in increasingly smaller changes to the weights on nodes in a deep neural network, leading to little or no learning. 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