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LSTM

Last updated May 21, 2025, generated by Sumble

What is LSTM?

LSTM (Long Short-Term Memory) is a type of recurrent neural network (RNN) architecture well-suited for processing sequential data. Unlike traditional RNNs, LSTMs are designed to handle the vanishing gradient problem, allowing them to learn long-range dependencies in sequences. They achieve this through a memory cell that can maintain information over extended periods, regulated by input, output, and forget gates. LSTMs are commonly used in natural language processing (e.g., machine translation, text generation), speech recognition, and time series analysis.

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What other technologies are related to LSTM?

LSTM Competitor Technologies

RNN

Recurrent Neural Networks (RNNs) are a broader class of networks that LSTM falls under. They are a competing approach for handling sequential data, though LSTMs address the vanishing gradient problem of basic RNNs.

GRU

Gated Recurrent Units (GRUs) are a simplified version of LSTMs, and serve as a competing approach for addressing the vanishing gradient problem in RNNs while often being faster to train. They are thus a competitor.

ARIMA

ARIMA (Autoregressive Integrated Moving Average) models are a traditional time series forecasting method and a statistical alternative for sequence modeling tasks, so can be considered a competitor.

Transformer

Transformers are a competing architecture for sequence modeling, often outperforming LSTMs on many tasks, especially those with long-range dependencies.

SARIMA

SARIMA (Seasonal Autoregressive Integrated Moving Average) is an extension of ARIMA for seasonal time series data, thus a competitor to LSTM for these tasks.

BERT

BERT is a transformer-based model that can be used for sequence modeling tasks such as natural language processing. It can be seen as a competitor for tasks where LSTMs would traditionally be used.

Transformers

Transformers (with a capital T) usually refers to the specific architecture, competing with LSTMs for sequence modeling.

Number of organizations that mention technology

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LSTM Complementary Technologies

Attention-based models

No summary available

Attention mechanisms can be used with LSTMs to focus on relevant parts of the input sequence, making them complementary. They are also used in transformer models, that can be considered as a competitor.

distributed training

Distributed training is a technique used to accelerate the training of large models, including LSTMs. It's complementary to the model architecture itself.

TensorFlow

TensorFlow is a deep learning framework that can be used to implement and train LSTM models. It's a tool that supports the use of LSTM, not a competitor.

Number of organizations that mention technology

1,196

LSTM

Attention-based models

distributed training

30,502

TensorFlow

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Which job functions commonly mention LSTM?

Machine Learning

0.7% of all Machine Learning jobs mention LSTM

View 691 jobs on Sumble

Applied Scientist

0.6% of all Applied Scientist jobs mention LSTM

View 73 jobs on Sumble

Data Scientist

0.5% of all Data Scientist jobs mention LSTM

View 1.2k jobs on Sumble

AI Engineer

0.5% of all AI Engineer jobs mention LSTM

View 412 jobs on Sumble

Data, Analytics & Machine Learning

0.1% of all Data, Analytics & Machine Learning jobs mention LSTM

View 2.5k jobs on Sumble

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Which organizations are mentioning LSTM?

Amazon

Retail Trade

JPMorgan Chase

Finance and Insurance

Amazon Web Services

Information

Qualcomm

Scientific and Technical Services

TikTok

Information

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**LSTM**

What is LSTM?

What other technologies are related to LSTM?

LSTM Competitor Technologies

LSTM Complementary Technologies

Which job functions commonly mention LSTM?

Which organizations are mentioning LSTM?

Find the right accounts, contact, message, and time to sell

LSTM