What are recurrent neural networks?

A recurrent neural network (RNN) is a type of artificial neural network which uses sequential data or time series data. These deep learning algorithms are commonly used for ordinal or temporal problems, such as language translation, natural language processing (nlp), speech recognition, and image captioning; they are incorporated into popular applications such as Siri, voice search, and Google Translate. Like feedforward and convolutional neural networks (CNNs), recurrent neural networks utilize training data to learn. They are distinguished by their “memory” as they take information from prior inputs to influence the current input and output. While traditional deep neural networks assume that inputs and outputs are independent of each other, the output of recurrent neural networks depend on the prior elements within the sequence. While future events would also be helpful in determining the output of a given sequence, unidirectional recurrent neural networks cannot account for these events in their predictions.

Let’s take an idiom, such as “feeling under the weather”, which is commonly used when someone is ill, to aid us in the explanation of RNNs. In order for the idiom to make sense, it needs to be expressed in that specific order. As a result, recurrent networks need to account for the position of each word in the idiom and they use that information to predict the next word in the sequence.

Another distinguishing characteristic of recurrent networks is that they share parameters across each layer of the network. While feedforward networks have different weights across each node, recurrent neural networks share the same weight parameter within each layer of the network. That said, these weights are still adjusted in the through the processes of backpropagation and gradient descent to facilitate reinforcement learning.

Recurrent neural networks leverage backpropagation through time (BPTT) algorithm to determine the gradients, which is slightly different from traditional backpropagation as it is specific to sequence data. The principles of BPTT are the same as traditional backpropagation, where the model trains itself by calculating errors from its output layer to its input layer. These calculations allow us to adjust and fit the parameters of the model appropriately. BPTT differs from the traditional approach in that BPTT sums errors at each time step whereas feedforward networks do not need to sum errors as they do not share parameters across each layer.

Through this process, RNNs tend to run into two problems, known as exploding gradients and vanishing gradients. These issues are defined by the size of the gradient, which is the slope of the loss function along the error curve. When the gradient is too small, it continues to become smaller, updating the weight parameters until they become insignificant—i.e. 0. When that occurs, the algorithm is no longer learning. Exploding gradients occur when the gradient is too large, creating an unstable model. In this case, the model weights will grow too large, and they will eventually be represented as NaN. One solution to these issues is to reduce the number of hidden layers within the neural network, eliminating some of the complexity in the RNN model.

Variant RNN architectures

• Bidirectional recurrent neural networks (BRNN): These are a variant network architecture of RNNs. While unidirectional RNNs can only drawn from previous inputs to make predictions about the current state, bidirectional RNNs pull in future data to improve the accuracy of it. If we return to the example of “feeling under the weather” earlier in this article, the model can better predict that the second word in that phrase is “under” if it knew that the last word in the sequence is “weather.”

• Long short-term memory (LSTM): This is a popular RNN architecture, which was introduced by Sepp Hochreiter and Juergen Schmidhuber as a solution to vanishing gradient problem. In their paper (link resides outside ibm.com), they work to address the problem of long-term dependencies. That is, if the previous state that is influencing the current prediction is not in the recent past, the RNN model may not be able to accurately predict the current state. As an example, let’s say we wanted to predict the italicized words in following, “Alice is allergic to nuts. She can’t eat peanut butter.” The context of a nut allergy can help us anticipate that the food that cannot be eaten contains nuts. However, if that context was a few sentences prior, then it would make it difficult, or even impossible, for the RNN to connect the information. To remedy this, LSTMs have “cells” in the hidden layers of the neural network, which have three gates–an input gate, an output gate, and a forget gate. These gates control the flow of information which is needed to predict the output in the network. For example, if gender pronouns, such as “she”, was repeated multiple times in prior sentences, you may exclude that from the cell state.

• Gated recurrent units (GRUs): This RNN variant is similar the LSTMs as it also works to address the short-term memory problem of RNN models. Instead of using a “cell state” regulate information, it uses hidden states, and instead of three gates, it has two—a reset gate and an update gate. Similar to the gates within LSTMs, the reset and update gates control how much and which information to retain.

What Makes RNN Special?

Recurrent neural networks (RNNs) set themselves apart from other neural networks with their unique capabilities:

• Internal Memory: This is the key feature of RNNs. It allows them to remember past inputs and use that context when processing new information.
• Sequential Data Processing: Because of their memory, RNNs are exceptional at handling sequential data where the order of elements matters. This makes them ideal for tasks like speech recognition, machine translation, natural language processing(nlp) and text generation.
• Contextual Understanding: RNNs can analyze the current input in relation to what they’ve “seen” before. This contextual understanding is crucial for tasks where meaning depends on prior information.
• Dynamic Processing: RNNs can continuously update their internal memory as they process new data. This allows them to adapt to changing patterns within a sequence.

RNNs are a type of neural network that has hidden states and allows past outputs to be used as inputs. They usually go like this:

Here’s a breakdown of its key components:

• Input Layer: This layer receives the initial element of the sequence data. For example, in a sentence, it might receive the first word as a vector representation.
• Hidden Layer: The heart of the RNN, the hidden layer contains a set of interconnected neurons. Each neuron processes the current input along with the information from the previous hidden layer’s state. This “state” captures the network’s memory of past inputs, allowing it to understand the current element in context.
• Activation Function: This function introduces non-linearity into the network, enabling it to learn complex patterns. It transforms the combined input from the current input layer and the previous hidden layer state before passing it on.
• Output Layer: The output layer generates the network’s prediction based on the processed information. In a language model, it might predict the next word in the sequence.
• Recurrent Connection: A key distinction of RNNs is the recurrent connection within the hidden layer. This connection allows the network to pass the hidden state information (the network’s memory) to the next time step. It’s like passing a baton in a relay race, carrying information about previous inputs forward.

RNN Applications

Recurrent neural networks (RNNs) shine in tasks involving sequential data, where order and context are crucial. Let’s explore some real-world use cases. Using RNN models and sequence datasets, you may tackle a variety of problems, including :

• Speech Recognition: RNNs power virtual assistants like Siri and Alexa, allowing them to understand spoken language and respond accordingly.
• Machine Translation: By analyzing sentence structure and context, RNNs translate languages more accurately, like Google Translate.
• Text Generation: RNNs are behind chatbots that can hold conversations and even creative writing tools that generate different text formats.
• Time Series Forecasting: RNNs analyze financial data to predict stock prices or weather patterns based on historical trends.
• Music Generation: RNNs can compose music by learning patterns from existing pieces and generating new melodies or accompaniments.
• Video Captioning: RNNs analyze video content and automatically generate captions, making video browsing more accessible.
• Anomaly Detection: RNNs can learn normal patterns in data streams (e.g., network traffic) and detect anomalies that might indicate fraud or system failures.
• Sentiment Analysis: By understanding the context and flow of text, RNNs can analyze sentiment in social media posts, reviews, or surveys.
• Stock Market Recommendation: RNNs can analyze market trends and news to suggest potential investment opportunities.
• Sequence study of the genome and DNA: RNNs can analyze sequential data in genomes and DNA to identify patterns and predict gene function or disease risk.

Conclusion

Recurrent Neural Networks (RNNs) are a powerful and versatile tool with a wide range of applications. They are commonly used in language modeling and text generation, as well as voice recognition systems. One of the key advantages of RNNs is their ability to process sequential data and capture long-range dependencies. When paired with Convolutional Neural Networks (CNNs), they can effectively create labels for untagged images, demonstrating a powerful synergy between the two types of neural networks.

However, one challenge with traditional RNNs is their struggle with learning long-range dependencies, which refers to the difficulty in understanding relationships between data points that are far apart in the sequence. This limitation is often referred to as the vanishing gradient problem. To address this issue, a specialized type of RNN called Long-Short Term Memory Networks (LSTM) has been developed, and this will be explored further in future articles. RNNs, with their ability to process sequential data, have revolutionized various fields, and their impact continues to grow with ongoing research and advancements.