Main Difference Between RNN and LSTM- (RNN vs LSTM) (2024)

Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) are two fundamental components in the field of deep learning. Neural networks serve as the computational model for deep learning. It is one of the most popular machine learning algorithms and outperforms other algorithms in accuracy and speed. Computers can make intelligent decisions with the help of neural networks and little human input. This blog will shed light on the RNN and LSTM differences that will explain the respective strengths and weaknesses in the field of sequential data analysis. So before we dive into the difference between RNN and LSTM, let’s first understand what a Neural Network is.

What is a Neural Network?

A neural network consists of various interconnected layers that work on the structure and function of the human brain. It learns from massive amounts of data and uses complex algorithms to train a neural network. Here is an illustration of how a dog’s attributes can be used to determine the breed of the dog.

• Two different dog breeds’ image pixels are supplied into the neural network’s input layer.
• The image pixels are then processed in hidden layers for feature extraction.
• The output layer produces a result that identifies whether it is a German Shepherd or a Labrador.
• Such networks do not require memorization of past output.

Different business difficulties can be helped by a variety of neural networks. We’ll examine a few of them now:

• Feed-Forward Neuron Network: Applied to generic classification and regression issues.
• Convolutional Neural Network: Utilized for object detection and picture categorization is the convolutional neural network (CNN).
• Deep Belief Network: Used in medicine to find cancer.
• RNN: Used for time series prediction, natural language processing, speech recognition, and voice recognition.

What is RNN (Recurrent Neural Network)?

It is important to emphasize that recurrent neural networks are made to analyze temporal or sequential data. These networks use additional data points in the sequence to make better predictions. They do this by taking input and reusing the activations of previous or later nodes in the series to affect the output. Entity extraction in the text is an excellent example of how data in different parts of a sequence can interact with each other.

For entities, the words that come before and after the entity in the sentence directly affect how they are classified. To work with temporal or sequential data such as sentences, we need to use algorithms designed to learn from past and “future data” in the sequence.

What is LSTM (Long-Term Short Memory)?

Long short-term memory (LSTM) is an artificial neural network in Artificial Intelligence and deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. Such a recurrent neural network (RNN) can process not only single data points (such as images) but also entire sequences of data (such as speech or video). The name LSTM refers to the analogy that a standard RNN has both “long-term memory” and “short-term memory”. The weights and biases of connections in the network change once per training episode, analogous to how physiological changes in synaptic strengths store long-term memories; activation patterns in the network change once per time step, analogous to how an instantaneous change in electrical firing patterns in the brain stores short-term memories.

The LSTM architecture aims to provide RNN with a short-term memory that can last thousands of steps, i.e., “long-term, short-term memory”. A standard LSTM unit consists of a cell, an input gate, an output gate, and a forgotten gate. The cell remembers values at arbitrary time intervals, and three gates regulate the flow of information into and out of the cell. LSTM networks are suitable for classifying, processing, and making predictions based on time series data because there can be delays of unknown duration between important events in the time series.

LSTMs were developed to deal with the vanishing gradient problem when training traditional RNNs. The relative insensitivity to gap length is an advantage of LSTMs over RNNs, hidden Markov models, and other sequential learning methods in many applications.

Difference Between RNN and LSTM

The main difference between LSTM and RNN lies in their ability to handle and learn from sequential data. LSTMs are more sophisticated and capable of handling long-term dependencies, making them the preferred choice for many sequential data tasks. Check out the comparison of LSTM vs RNN in the below table.

These are the 9 major LSTM and RNN differences that highlight the superiority of LSTMs in handling sequential data. Now, read out some advantages of Long Short-Term Memory networks.

Advantages of LSTM

The main Advantages of LSTM that RNN might miss are:-

• RNN training
• The vanishing or exploding gradient problem
• Slow and complex training procedures
• Complicated processing of longer sequences

RNN Training

The image above shows quite nicely what a typical RNN block looks like. As you can see, RNNs take the output of the previous node as input, even in the current state. It helps in the proper context but fails in one aspect. Minimization of the loss function. Now, the problem with these activation functions is that the weights (or gradients) make the process a bit more complicated whenever they are used in sequence training.

Learners Also Read: What are optimization techniques in machine learning?

The Vanishing of Exploding Gradient Problem

Problems with vanishing and/or exploding gradients occur regularly with RNNs. They occur because it is hard to capture long-range conditions due to the multiplicative angle, which can shrink/broaden dramatically with the number of layers. Thus, if the sequence is too long, the model can train with zero weights (i.e., no training) or exploding weights.
The gradient vanishes if the biggest eigenvalue is less than 1. The gradient explodes if the biggest eigenvalue is bigger than 1.

Clipping gradients is a method for preventing exploding gradients. As the name suggests, transitions are clipped once they reach a predefined threshold. However, the problem of vanishing gradients still exists. This was later solved up to a point by the introduction of LSTM networks.

Slow and Complex Training Procedures

RNNs have many fundamental issues, one of which is their repeated nature. This means they take a lot of time to train. The overall training speed of RNNs is relatively low compared to feedforward networks. Second, since the RNN needs to calibrate the previous outputs and the current inputs to the state change function per node, it is pretty tricky to implement. The complexity of training sometimes makes it challenging to adapt to RNN training.

Complicated Processing of Longer Sequences

As mentioned earlier, training RNNs on very long sequences is challenging, especially when using ReLU or tanh activations. This is another reason for the introduction of GRU-based networks.

Applications of LSTM

LSTM models must be trained on a training dataset before being used in real-world applications. Some of the most demanding applications are described below:

• Language modeling or text generation involves computing words when given a sequence of words as input. Language models can be run at the level of characters, n-grams, sentences, or even paragraphs.
• Image processing involves performing image analysis and wrapping its result into a sentence. To accomplish this, a dataset with a large number of photos and matching descriptive labels is required. The features of the photos in the dataset are predicted using the model that has already been trained. Data for a photo. The most suggestive terms are then removed from the dataset through processing. Data in text format. We try to fit the model with these two sorts of data. The task of the model is to generate a descriptive sentence for the image one word at a time by taking the input words that have been pre-predicted by the model and the image.
• Speech and handwriting recognition
• Music generation is quite similar to text generation, where LSTMs predict musical notes instead of text by analyzing the combination of given notes as input.
• A sequence in one language is mapped to a sequence in another language during language translation. Similar to image processing, only a portion of the dataset including the phrases and their translations are used to train the model. The input sequence is converted to a vector representation (encoding) before being output as a translated version using an LSTM encoder-decoder model.

Applications of Recurrent Neural Network

Let’s look at an example of an RNN in use now that you are familiar with what it is.

• RNN can be used to create a deep learning model that can translate text between languages without the assistance of a human. You could convert a text from your mother tongue to English, for instance.
• A deep learning model for text production can also be created using RNNs. A trained model learns the probability that a word or character will appear based on the prior sequence of words or characters used in the text. The character, n-gram, phrase, or paragraph levels can all be used to train a model.
• Image captioning is the process of writing text that describes the subject matter of an image. The image’s content may show both the object and the object’s behavior as it appears in the picture.
• This also goes by the name Automatic Speech Recognition (ASR), and it can translate spoken language into written or text format. Don’t confuse voice recognition with speech recognition; voice recognition recognizes the user’s voice. However, speech recognition focuses on turning audio input into text.

Conclusion

The main difference between RNN and LSTM (Long Short-Term Memory) lies in their ability to effectively handle long-range dependencies in sequential data. While RNNs suffer from vanishing gradient problems, LSTMs employ a more sophisticated architecture with memory cells and gating mechanisms. This allows them to capture and retain important information over longer sequences. This key distinction makes LSTMs a preferred choice for various applications requiring sequential data analysis and prediction.