Last updated on Jan 18, 2024
- All
- Engineering
- Machine Learning
Powered by AI and the LinkedIn community
Cisco sponsors Machine Learning collaborative articles.
Sponsorship does not imply endorsem*nt. LinkedIn's editorial content maintains complete independence.
1
RNN basics
2
LSTM basics
3
RNN vs LSTM
4
Other alternatives
5
Experiment and evaluate
6
Here’s what else to consider
Natural language processing (NLP) is a branch of machine learning that deals with understanding and generating natural language texts, such as speech, tweets, reviews, or emails. NLP tasks often involve sequential data, where the order and context of the words matter. For example, sentiment analysis, machine translation, text summarization, and speech recognition are all NLP tasks that require sequential data. To handle sequential data, you need a model that can capture the temporal dependencies and patterns in the data. Two common types of models for sequential data are recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. But how do you choose between them for your NLP task? In this article, we will compare RNNs and LSTMs, and give you some tips on how to decide which one to use.
Top experts in this article
Selected by the community from 11 contributions. Learn more
Earn a Community Top Voice badge
Add to collaborative articles to get recognized for your expertise on your profile. Learn more
- Priya Ranjani Mohan Management Consultant at KPMG | Samsung's AI Innovation Program Member | Speaker | LinkedIn Creator Accelerator Program…
21
- Danny Diaz ML Protein Engineer @ IFML | Entrepreneur
7
- Venkat P. Data enthusiast, passionate about designing and building scalable and efficient data infrastructures that drive…
5
1 RNN basics
RNNs are neural networks that have a looping structure, where the output of one step is fed back as an input to the next step. This allows RNNs to process sequential data, as they can maintain a hidden state that encodes the previous information. RNNs can be trained using backpropagation through time (BPTT), which is a variant of the standard backpropagation algorithm that updates the weights across the time steps. RNNs can be applied to various NLP tasks, such as language modeling, text classification, and sequence labeling.
Help others by sharing more (125 characters min.)
- Venkat P. Data enthusiast, passionate about designing and building scalable and efficient data infrastructures that drive business insights and decisions.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Recurrent Neural Networks (RNNs) are a class of neural networks that are designed to operate on sequences of data such as text, speech, and time-series data. Unlike traditional feedforward neural networks, RNNs have feedback connections that allow them to maintain a state or memory of previous inputs.The basic idea of RNNs is to process a sequence of inputs one at a time, and for each input, update the internal state of the network. The output of the network at each time step can then depend not only on the current input but also on the previous inputs and the internal state of the network.One of the key features of RNNs is that they share weights across time steps, which allows them to efficiently process sequences of arbitrary length.
LikeLike
Celebrate
Support
Love
Insightful
Funny
5
- Riyaj Muhammad Research Engineer | Kaggle Competition and discussion Expert |NIT Raipur@2021
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
RNN brings a very new aspects with respect to older neural net. That makes it easier to use sequential data to build neural net. And that is "memory". They are very useful till date for small sequences. The drawback is that the memory mechanism lacks the capability of dumping the non useful signals.And when larger sequence appears exploding and vanishing gradient makes them very inefficient to remember older information.
See AlsoChatGPT and the Stock Market | YellowApplying machine learning algorithms to predict the stock price trend in the stock market – The case of VietnamMachine Learning for Stock Price PredictionA performance comparison of machine learning models for stock market prediction with novel investment strategyLikeLike
Celebrate
Support
Love
Insightful
Funny
2 LSTM basics
LSTMs are a special kind of RNNs that have a more complex structure, where each unit has three gates: an input gate, an output gate, and a forget gate. These gates control how much information is allowed to enter, leave, or be forgotten by the unit. LSTMs can learn long-term dependencies and avoid the vanishing gradient problem, which is a common issue in RNNs where the gradients become too small to update the weights effectively. LSTMs can also handle variable-length sequences and bidirectional inputs, which are useful for NLP tasks such as machine translation, text generation, and sentiment analysis.
Help others by sharing more (125 characters min.)
- Priya Ranjani Mohan Management Consultant at KPMG | Samsung's AI Innovation Program Member | Speaker | LinkedIn Creator Accelerator Program Alumni | AI Strategist
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
The cool aspect about LSTM (Long Short-Term Memory) is that has a special ability to remember important information from the past, which can help it make better predictions about what will happen in the future.It's like having a really good memory that can remember important things from a long time ago.Here are some examples of how LSTM can be used:Text prediction: LSTM can be trained on a large corpus of text to predict the next word in a sentence or even generate entire paragraphs of text that are similar to the original training data.Speech recognition: LSTM can be used to recognize speech by processing the audio waveform in small segments and predicting the corresponding phonemes or words.
LikeLike
Celebrate
Support
Love
Insightful
Funny
21
- Abhinav kumar Data Scientist | Mentor | Public Speaker | On Mission to empower 1M Data Scientists | Entrepreneur | Martial Artist | Stock Marketer | Generative AI | Social Activist | Open Source Contributor | Blogger | Storyteller
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
LSTM the "Long Short Term Memory" is a special kind of algorithm widely used in NLP and Forecasting, It can remember pieces of information from the past for a long and also it resolves the issue of the vanishing gradient problem of the RNN (Recurrent Neural Network). Due to long memory, it is more accurate than other algorithms like RNN.
LikeLike
Celebrate
Support
Love
Insightful
Funny
3 RNN vs LSTM
When choosing between RNNs and LSTMs, there are several factors to consider. RNNs are simpler and faster to train than LSTMs, as they have fewer parameters and computations. However, LSTMs can learn more complex and long-range patterns. RNNs have a limited memory capacity, while LSTMs can selectively remember or forget the relevant information. Additionally, RNNs are more prone to overfitting than LSTMs, as they have less regularization and more bias. Thus, if your data is relatively simple and short, you may prefer RNNs; if it is complex and long, you may prefer LSTMs; if it is small and noisy, you may prefer LSTMs; and if it is large and clean, you may prefer RNNs.
Help others by sharing more (125 characters min.)
- Danny Diaz ML Protein Engineer @ IFML | Entrepreneur
(edited)
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
If you are doing any form of NLP you should use attention-based networks,such as a transformer. While everything discussed about RNNs and LSTMs is accurate, they are deprecated and you should use the latest techniques and frameworks.
LikeLike
Celebrate
Support
Love
Insightful
Funny
7
- Karl Swanson Physician, Data Scientist, Building AI Enabled Clinician Workflow Tools at Quench, Inc.
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
A few people are mentioning that attention based NNs are the only NLP solution nowadays. While transformers are great there is some evidence to show that scaling RNNs like RWKV can perform just as well and do not suffer the quadratic memory problem traditional attention mechanisms have.
LikeLike
Celebrate
Support
Love
Insightful
Funny
3
Load more contributions
4 Other alternatives
RNNs and LSTMs are not the only models for sequential data. There are other variants and extensions of RNNs and LSTMs that may suit your needs better. For example, gated recurrent units (GRUs) are a simplified version of LSTMs that have only two gates instead of three. GRUs are easier to implement and train than LSTMs, and may perform similarly or better on some tasks. Another example is attention mechanisms, which are a way of enhancing RNNs and LSTMs by allowing them to focus on the most relevant parts of the input or output sequences. Attention mechanisms can improve the accuracy and efficiency of NLP tasks such as machine translation, text summarization, and question answering.
Help others by sharing more (125 characters min.)
-
(edited)
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
TLDR, Don't use RNNs or LSTMs in production. They're good to learn and understand when you're entering the world of NLP, but are in fact, riddled with a host of problems. In fact, they'll most likely fail for tasks such as machine translation and summarization.There's far more advanced and a wide, ever-increasing variety of alternatives, which have fixed some of these problems, such as VAEs, Transformer models, etc.Explore these alternatives for your specific use-case.
LikeLike
Celebrate
Support
Love
Insightful
Funny
1
-
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Transformer models like BERT and GPT are currently at the forefront of new trends in NLP, thanks to their ability to capture context, understand long-range dependencies, and exhibit remarkable versatility.A key breakthrough of transformers is their ability to capture contextual information effectively and efficiently. Unlike sequential models like RNNs and LSTMs, transformers process input data in parallel, allowing them to consider all parts of a sentence simultaneously. Moreover, transformer models employ a mechanism called "attention", which enables then to capture long-range dependencies in data often more effectively than alternatives.
LikeLike
Celebrate
Support
Love
Insightful
Funny
5 Experiment and evaluate
The best way to choose between RNNs and LSTMs for your NLP task is to experiment and evaluate different models on your data. You can use frameworks such as TensorFlow, PyTorch, or Keras to implement and compare RNNs and LSTMs easily. You can also use metrics such as accuracy, precision, recall, F1-score, or perplexity to measure the performance of your models on your task. You can also use visualizations such as confusion matrices, heat maps, or attention weights to analyze the behavior and errors of your models. By experimenting and evaluating different models, you can find the optimal balance between complexity, memory, data size, and other factors for your NLP task.
Help others by sharing more (125 characters min.)
- Priya Ranjani Mohan Management Consultant at KPMG | Samsung's AI Innovation Program Member | Speaker | LinkedIn Creator Accelerator Program Alumni | AI Strategist
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Use RNN for short-term dependencies and LSTM for long-term dependencies.Sentiment analysis: RNN might be more useful for analyzing short sentences and tweets to determine sentiment, while LSTM could be better suited for analyzing longer text passages where the sentiment might change over time.Music generation: RNN could be useful for generating short musical phrases, while LSTM might be more suited for generating longer pieces of music with complex structure and dynamics.Speech recognition: RNN could be useful for recognizing short phrases and simple commands, while LSTM could be better suited for recognizing longer, more complex sentences with a wider range of vocabulary.
LikeLike
Celebrate
Support
Love
Insightful
Funny
6
6 Here’s what else to consider
This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?
Help others by sharing more (125 characters min.)
- Riyaj Muhammad Research Engineer | Kaggle Competition and discussion Expert |NIT Raipur@2021
- Report contribution
Thanks for letting us know! You'll no longer see this contribution
Although we are living in the age of transformers and LLMs but LSTM and RNN still holds there relevancy. They are still useful for signal detection and time series predictions. Apart from this they should be used for small sequence NLP tasks.
LikeLike
Celebrate
Support
Love
Insightful
Funny
Machine Learning
Machine Learning
+ Follow
Rate this article
We created this article with the help of AI. What do you think of it?
It’s great It’s not so great
Thanks for your feedback
Your feedback is private. Like or react to bring the conversation to your network.
Tell us more
Tell us why you didn’t like this article.
If you think something in this article goes against our Professional Community Policies, please let us know.
We appreciate you letting us know. Though we’re unable to respond directly, your feedback helps us improve this experience for everyone.
If you think this goes against our Professional Community Policies, please let us know.
More articles on Machine Learning
No more previous content
- What do you do if your Machine Learning team has diverse skill sets and backgrounds? 31 contributions
- What do you do if your Machine Learning project needs feedback from end-users? 50 contributions
- What do you do if delegated tasks in Machine Learning are not completed successfully? 17 contributions
- What do you do if machine learning failures spark innovation and creativity? 12 contributions
- What do you do if your machine learning research findings aren't reaching the wider scientific community? 17 contributions
- What do you do if you're faced with common Machine Learning interview questions and need to prepare? 16 contributions
- What do you do if your machine learning project is falling behind schedule due to ineffective communication? 12 contributions
- What do you do if your Machine Learning interview requires critical and analytical thinking? 8 contributions
- What do you do if you're an executive facing challenges in Machine Learning and need to overcome them? 7 contributions
- What do you do if your business growth is stagnant and you're a machine learning expert? 14 contributions
- What do you do if you want to boost your career in Machine Learning by joining online communities and forums? 12 contributions
No more next content
Explore Other Skills
- Web Development
- Programming
- Agile Methodologies
- Software Development
- Computer Science
- Data Engineering
- Data Analytics
- Data Science
- Artificial Intelligence (AI)
- Cloud Computing
More relevant reading
- Neural Networks How do self-attention and recurrent models compare for natural language processing tasks?
- Machine Learning How can you prevent overfitting when training an NLP model for ML?
- Artificial Neural Networks What are the advantages and disadvantages of CNN over ANN for natural language processing?
- Machine Learning What are the best NLP models for question answering?
Help improve contributions
Mark contributions as unhelpful if you find them irrelevant or not valuable to the article. This feedback is private to you and won’t be shared publicly.
Contribution hidden for you
This feedback is never shared publicly, we’ll use it to show better contributions to everyone.