name: inverse layout: true class: center, middle, inverse <div class="my-header"><span> <a href="/training-material/topics/statistics" title="Return to topic page" ><i class="fa fa-level-up" aria-hidden="true"></i></a> <a class="nav-link" href="https://github.com/galaxyproject/training-material/edit/main/topics/statistics/tutorials/RNN/slides.html"><i class="fa fa-pencil" aria-hidden="true"></i></a> </span></div> <div class="my-footer"><span> <img src="/training-material/assets/images/GTN-60px.png" alt="Galaxy Training Network" style="height: 40px;"/> </span></div> --- <img src="/training-material/assets/images/GTN.png" alt="Galaxy Training Network" class="cover-logo"/> # Recurrent neural networks (RNN) Deep Learning - Part 2 <div markdown="0"> <div class="contributors-line"> Authors: <a href="/training-material/hall-of-fame/kxk302/" class="contributor-badge contributor-kxk302"><img src="https://avatars.githubusercontent.com/kxk302?s=27" alt="Avatar">Kaivan Kamali</a> </div> </div> <!-- modified date --> <div class="footnote" style="bottom: 8em;"><i class="far fa-calendar" aria-hidden="true"></i><span class="visually-hidden">last_modification</span> Updated: May 27, 2021</div> <!-- other slide formats (video and plain-text) --> <div class="footnote" style="bottom: 6em;"> <i class="fas fa-file-alt" aria-hidden="true"></i><span class="visually-hidden">text-document</span><a href="slides-plain.html"> Plain-text slides</a> </div> <!-- usage tips --> <div class="footnote" style="bottom: 2em;"> <strong>Tip: </strong>press <kbd>P</kbd> to view the presenter notes | <i class="fa fa-arrows" aria-hidden="true"></i><span class="visually-hidden">arrow-keys</span> Use arrow keys to move between slides </div> ??? 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Useful when presenting. --- ## Requirements Before diving into this slide deck, we recommend you to have a look at: - [Introduction to Galaxy Analyses](/training-material/topics/introduction) - [Statistics and machine learning](/training-material/topics/statistics) - Introduction to deep learning: [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/statistics/tutorials/intro_deep_learning/tutorial.html) - Deep Learning (Part 1) - Feedforward neural networks (FNN): [<i class="fab fa-slideshare" aria-hidden="true"></i><span class="visually-hidden">slides</span> slides](/training-material/topics/statistics/tutorials/FNN/slides.html) - [<i class="fas fa-laptop" aria-hidden="true"></i><span class="visually-hidden">tutorial</span> hands-on](/training-material/topics/statistics/tutorials/FNN/tutorial.html) --- ### <i class="far fa-question-circle" aria-hidden="true"></i><span class="visually-hidden">question</span> Questions - What is a recurrent neural network (RNN)? - What are some applications of RNN? --- ### <i class="fas fa-bullseye" aria-hidden="true"></i><span class="visually-hidden">objectives</span> Objectives - Understand the difference between feedforward neural networks (FNN) and RNN - Learn various RNN types and architectures - Learn how to create a neural network using Galaxy’s deep learning tools - Solve a sentiment analysis problem on IMDB movie review dataset using RNN in Galaxy --- # What is a recurrent neural network (RNN)? ??? What is a recurrent neural network (RNN)? --- # Recurrent Neural Network (RNN) - RNN models sequential data (temporal/ordinal) - In RNN, training example is a sequence, which is presented to RNN one at a time - E.g., sequence of English words is passed to RNN, one at a time - And, RNN generates a sequence of Persian words, one at a time - In RNN, output of network at time *t* is used as input at time *t+1* - RNN applied to image description, machine translation, sentiment analysis, etc. --- # One-to-many RNN  <!-- https://pixy.org/3013900/ CC0 license--> --- # Many-to-one RNN  <!-- https://pixy.org/3013900/ CC0 license--> --- # Many-to-many RNN  <!-- https://pixy.org/3013900/ CC0 license--> --- # RNN architectures - Vanilla RNN - Suffers from *vanishing gradient* problem - LSTM and GRU - Uses *gates* to avoid vanishing gradient problem --- # Sentiment analysis - We perform sentiment analysis on IMDB movie reviews dataset - Train RNN on training dataset (25000 positive/negative movie reviews) - Test RNN on test set (25000 positive/negative movie reviews) - Training and test sets have no overlap - Since dealing with text data, good to review mechanisms for representing text data --- # Text preprocessing - Tokenize a document, i.e., break it down into words - Remove punctuations, URLs, and stop words (‘a’, ‘of’, ‘the’, etc.) - Normalize the text, e.g., replace ‘brb’ with ‘Be right back’, etc - Run the spell checker to fix typos - Make all words lowercase --- # Text preprocessing - Perform stemming/lemmatization - If we have words like ‘organizer’, ‘organize’, and ‘organized’ - Want to reduce all of them to a single word - Stemming cuts end of these words for a single root - E.g., ‘organiz’. May not be an actual word - Lemmatization reduces to a root that is actually a word - E.g., ‘organize’ --- # Bag of words (BoW) - If you don’t care about the order of the words in a document - 2D array. Rows represent documents. Columns represent words in vocabulary - All unique words in all documents - If a word not present in a document, we have a zero at row and column entry - If a word is present in a document, we have a one at row and colum entry - Or, we could use the word count or frequency --- # Bag of words (BoW) - Document 1: Magic passed the basketball to Kareem - Document 2: Lebron stole the basketball from Curry  <!-- https://pixy.org/3013900/ CC0 license--> - BoW is simple, but does not consider rarity of words across documents - Important for document classification --- # Term frequency inverse document frequency (TF-IDF) - If you don’t care about the order of the words in a document - Similar to BoW, we have an entry for each document-word pair - Entry is product of - Term frequency, frequency of a word in a document, and - Inverse document frequency, total number of documents divided by number of documents that have word - Usually use logarithm of the IDF - TF-IDF takes into account rarity of a word across documents --- # One-hot encoding (OHE) - Technique to convert categorical variables such as words into a vector - Suppose our vocabulary has 3 words: orange, apple, banana - Each word is represented by a vector of size 3  <!-- https://pixy.org/3013900/ CC0 license--> - OHE problems - For very large vocabulary sizes requires tremendous amount of storage - Also, no concept of word similarity --- # Word2Vec - Each word represented as an *n* dimensional vector - *n* much smaller than vocabulary size - Words that have similar meanings are close in vector space - Words considered similar if they co-occur often in documents - Two Word2Vec architectures - Continuous BoW - predicts probability of a word given the surrounding words - Continuous skip-gram - given a word predicts probability of the surrounding words --- # Sentiment analysis - Sentiment classification of IMDB movie reviews with RNN - Train RNN using IMDB movie reviews - Goal is to learn a model such that given a review we predict whether review is positive/negative - We evaluate the trained RNN on test dataset and plot confusion matrix --- # For references, please see tutorial's References section --- --- ## Thank You! This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors! <div markdown="0"> <div class="contributors-line"> Authors: <a href="/training-material/hall-of-fame/kxk302/" class="contributor-badge contributor-kxk302"><img src="https://avatars.githubusercontent.com/kxk302?s=27" alt="Avatar">Kaivan Kamali</a> </div> </div> <div style="display: flex;flex-direction: row;align-items: center;justify-content: center;"> <img src="/training-material/assets/images/GTN.png" alt="Galaxy Training Network" style="height: 100px;"/> </div> <a rel="license" href="https://creativecommons.org/licenses/by/4.0/"> This material is licensed under the Creative Commons Attribution 4.0 International License</a>.