--- title: Understanding Seq2Seq Models: From RNNs to LSTMs, GRUs,... description: A beginner-friendly walkthrough of the sequence modeling family tree, starting from why feedforward networks fail at sequential data. Covers RNNs and their... canonical: https://daily.dev/posts/understanding-seq2seq-models-from-rnns-to-lstms-grus-and-beyond-omsfw8ieq twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Understanding Seq2Seq Models: From RNNs to LSTMs, GRUs, and Beyond | daily.dev og:description: A beginner-friendly walkthrough of the sequence modeling family tree, starting from why feedforward networks fail at sequential data. Covers RNNs and their... og:url: https://daily.dev/posts/understanding-seq2seq-models-from-rnns-to-lstms-grus-and-beyond-omsfw8ieq og:image: https://api.daily.dev/og/posts/oMsfw8IEq.png og:image:alt: Understanding Seq2Seq Models: From RNNs to LSTMs, GRUs, and Beyond og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Understanding Seq2Seq Models: From RNNs to LSTMs, GRUs, and Beyond **[Medium](https://daily.dev/sources/medium_js)** · 10 min read · 0 upvotes · 0 comments ## Summary A beginner-friendly walkthrough of the sequence modeling family tree, starting from why feedforward networks fail at sequential data. Covers RNNs and their vanishing gradient problem, LSTMs with their three-gate cell-state architecture, GRUs as a lighter two-gate alternative, Bidirectional RNNs for full-context understanding, and finally Seq2Seq encoder-decoder models. Includes a comparison table and closes with a note on how the context-vector bottleneck motivated the Attention mechanism and Transformers. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What is the main difference between an LSTM and a GRU? An LSTM uses three gates (forget, input, output) and maintains two separate memory tracks, a cell state and a hidden state, while a GRU merges these into a single hidden state and uses only two gates (reset and update). This gives GRUs roughly 25-30% fewer parameters and faster training, with comparable performance on most tasks, though LSTMs can edge ahead on very long-range dependencies. _Comparing LSTM and GRU trade-offs before picking one for a sequence model is easier with daily.dev's developer content._ ### Why do RNNs struggle with long sequences? Recurrent neural networks suffer from vanishing gradients during backpropagation through time, where the error signal shrinks as it travels back through many time steps, so early inputs barely get updated. In practice, a plain RNN might retain the last 3-5 words well but lose the influence of word one by around word twenty, making long-range tasks like translation difficult. _Developers debugging long-sequence models can find deeper explainers like this via daily.dev._ ### Why can't a bidirectional RNN be used for real-time streaming predictions? A bidirectional RNN requires the entire input sequence to be available upfront because it runs one recurrent layer forward and another backward, then combines their hidden states at each step. Since future inputs aren't available during live streaming tasks like real-time speech-to-text, bidirectional RNNs are limited to scenarios where the full sequence, like a written sentence, is already known. _daily.dev surfaces explainers like this for developers choosing architectures for streaming versus batch sequence tasks._ ## Similar posts on daily.dev - [RNN vs LSTM vs GRU for Sequential Data](https://daily.dev/posts/rnn-vs-lstm-vs-gru-for-sequential-data-xruszopis) · Medium · 0 upvotes · 0 comments - [Recurrent Neural Networks \(RNNs\) From First Principles: The Complete Beginner Guide \(Part 1/2\)](https://daily.dev/posts/recurrent-neural-networks-rnns-from-first-principles-the-complete-beginner-guide-part-1-2--7plwxi8fp) · Medium · 0 upvotes · 0 comments - [CNNs, RNNs, and Transformers Explained: A Mental Model for Key Deep Learning Concepts](https://daily.dev/posts/cnns-rnns-and-transformers-explained-a-mental-model-for-key-deep-learning-concepts-pxbxlxvct) · freeCodeCamp · 3 upvotes · 0 comments - [The Problem with Long Sequences: Why Transformers Needed Attention](https://daily.dev/posts/the-problem-with-long-sequences-why-transformers-needed-attention-etumffuqu) · Medium · 0 upvotes · 0 comments --- Tags: [#deep-learning](https://daily.dev/tags/deep-learning), [#nlp](https://daily.dev/tags/nlp), [#lstm](https://daily.dev/tags/lstm) [View this post on daily.dev](https://daily.dev/posts/understanding-seq2seq-models-from-rnns-to-lstms-grus-and-beyond-omsfw8ieq) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Understanding Seq2Seq Models: From RNNs to LSTMs, GRUs, and Beyond","url":"https://daily.dev/posts/understanding-seq2seq-models-from-rnns-to-lstms-grus-and-beyond-omsfw8ieq","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/understanding-seq2seq-models-from-rnns-to-lstms-grus-and-beyond-omsfw8ieq"},"datePublished":"2026-07-22T07:32:45.485Z","dateModified":"2026-09-13T18:40:14.444Z","description":"A beginner-friendly walkthrough of the sequence modeling family tree, starting from why feedforward networks fail at sequential data. Covers RNNs and their...","image":"https://media.daily.dev/image/upload/s--OHB84bZF--/f_auto/v1722860399/public/Placeholder%2010","thumbnailUrl":"https://media.daily.dev/image/upload/s--OHB84bZF--/f_auto/v1722860399/public/Placeholder%2010","isAccessibleForFree":true,"articleSection":"Medium","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Medium","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/medium","url":"https://daily.dev/sources/medium_js"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/understanding-seq2seq-models-from-rnns-to-lstms-grus-and-beyond-omsfw8ieq","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"deep-learning,nlp,lstm","timeRequired":"PT10M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Medium","item":"https://daily.dev/sources/medium_js"},{"@type":"ListItem","position":3,"name":"Understanding Seq2Seq Models: From RNNs to LSTMs, GRUs, and Beyond"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/understanding-seq2seq-models-from-rnns-to-lstms-grus-and-beyond-omsfw8ieq#faq","mainEntity":[{"@type":"Question","name":"What is the main difference between an LSTM and a GRU?","acceptedAnswer":{"@type":"Answer","text":"An LSTM uses three gates (forget, input, output) and maintains two separate memory tracks, a cell state and a hidden state, while a GRU merges these into a single hidden state and uses only two gates (reset and update). This gives GRUs roughly 25-30% fewer parameters and faster training, with comparable performance on most tasks, though LSTMs can edge ahead on very long-range dependencies. Comparing LSTM and GRU trade-offs before picking one for a sequence model is easier with daily.dev's developer content."}},{"@type":"Question","name":"Why do RNNs struggle with long sequences?","acceptedAnswer":{"@type":"Answer","text":"Recurrent neural networks suffer from vanishing gradients during backpropagation through time, where the error signal shrinks as it travels back through many time steps, so early inputs barely get updated. In practice, a plain RNN might retain the last 3-5 words well but lose the influence of word one by around word twenty, making long-range tasks like translation difficult. Developers debugging long-sequence models can find deeper explainers like this via daily.dev."}},{"@type":"Question","name":"Why can't a bidirectional RNN be used for real-time streaming predictions?","acceptedAnswer":{"@type":"Answer","text":"A bidirectional RNN requires the entire input sequence to be available upfront because it runs one recurrent layer forward and another backward, then combines their hidden states at each step. Since future inputs aren't available during live streaming tasks like real-time speech-to-text, bidirectional RNNs are limited to scenarios where the full sequence, like a written sentence, is already known. daily.dev surfaces explainers like this for developers choosing architectures for streaming versus batch sequence tasks."}}]} ```