Everything you know about Recurrent Neural Networks (RNN)- Part I

Imagine you’re writing an email to a colleague.

• You Typed: “Can we schedule a meeting”
• Smart Compose Suggests: “for next week?”
• You Accept: Press tab to complete the sentence.

How Does Gmail Know?

This “magic” happens thanks to Recurrent Neural Networks (RNNs). Here’s a simplified explanation. RNNs process the sequence of words you’ve typed and understand the context. When you type “Can we schedule a meeting,” the RNN recognizes that a time frame is likely to follow. Let’s dive deeper into how RNNs work. In this first part, we’ve covered the theory — focusing on RNN architecture, key activation functions, and their comparison with transformers.

In the next part, we’ll move to practical applications, showing how to train RNNs for real-world tasks, step by step.

So, What is exactly RNN?

Recurrent neural networks, also known as RNNs, are deep neural networks that have been trained on time series or sequential data in order to develop machine learning models that can provide sequential conclusions or predictions based on sequential inputs.

RNNs are effective at understanding information sequences such as sentences or time-based data. Imagine it similar to a person who recalls the past in order to make better predictions about the future. For example, if you are reading a narrative, understanding the previous sentences can help you better grasp the present one. RNNs operate in a similar manner, leveraging prior knowledge to make predictions or conclusions about new data.

This impressive capability is made possible by Recurrent Neural Networks (RNNs). Here’s a simplified explanation:

· Context Understanding: RNNs analyse the sequence of words you entered and determine the context. When you input “Can we schedule a meeting,” the RNN anticipates a time.

· Pattern Recognition: Millions of emails have been used to train the RNN to recognise common patterns. It recognises that “for next week?” is a common response to scheduling requests.

· Memory: RNNs remember preceding words in a phrase, allowing them to make accurate predictions based on the whole context.

· Personalisation: The more you use Smart Compose, the more accurate it becomes at anticipating your individual writing style and preferences, making recommendations more relevant.

& How Recurrent Neural Networks work?

RNN works on the principle of saving the output of a particular layer and feeding this back to the input in order to predict the output of the layer.

They are designed to remember and reuse information from previous steps in a sequence, which makes them particularly powerful for tasks where context matters, like language processing or time series predictions. The way they work is by taking the output of a layer and feeding it back into the network as input, helping to predict future outputs.

In simple terms, RNNs have a loop that passes information through the network, especially through the hidden layers, which are responsible for processing and storing the data. The input layer, often called “x,” takes in the data and passes it to the hidden layer for further processing.

The hidden layer, “h,” can have multiple layers, each with its own set of weights, biases, and activation functions that determine how the input is processed. In traditional neural networks, each layer works independently of the others, meaning the network doesn’t “remember” anything from previous layers. But in an RNN, the network has a memory component, which allows it to use information from previous steps to influence the current processing.

Rather than creating multiple hidden layers, an RNN loops through the same hidden layer multiple times, adjusting its parameters as needed. This looping mechanism helps the network handle sequences of data more efficiently by making use of standardized activation functions, weights, and biases across the loop. In general, the information in RNNs is looped through to the middle hidden layer.

Key Activation Functions in Recurrent Neural Networks (RNNs)

Activation functions are essential for adding non-linearity to Recurrent Neural Networks (RNNs), which enables the model to recognise intricate patterns and correlations in the input. The following are a few typical activation functions for RNNs:

• Sigmoid Function: Often used in RNNs, the sigmoid function maps input values between 0 and 1, making it ideal for binary classification tasks. It helps determine how much information to pass on or forget in LSTM and GRU networks.

Formula: σ(x) = 1 / (1 + e^(-x))

• Tanh (Hyperbolic Tangent) Function: Another popular choice, the tanh function outputs values between -1 and 1, making it effective for tasks where a broader range of outputs is needed. It’s commonly used in LSTMs to handle more complex relationships.

Formula: tanh(x) = (e^x — e^(-x)) / (e^x + e^(-x))

• ReLU (Rectified Linear Unit) Function: Widely used in deep networks, ReLU provides a fast and efficient activation, outputting values between 0 and infinity. This helps in preventing the vanishing gradient problem in RNNs.

Formula: ReLU(x) = max(0, x)

• Leaky ReLU Function: A variant of ReLU, Leaky ReLU introduces a small slope for negative values, addressing the “dead neuron” problem by allowing a small, non-zero gradient for negative inputs.

Formula: Leaky ReLU(x) = max(0.01x, x)

• Softmax Function: Commonly used in the output layer of RNNs for multi-class classification tasks, softmax converts the raw model outputs into a probability distribution across the possible classes.

Formula: softmax(x) = e^x / ∑(e^x)

Key Types of RNNs and Their Applications

1. Vanilla RNN: The simplest form of RNN, where each neuron processes the input step by step, looping back information. However, Vanilla RNNs struggle with long-term dependencies, as they tend to forget earlier parts of a sequence when processing long inputs. In Smart Compose, for instance, a basic Vanilla RNN may have difficulty recalling the start of your sentence when the sequence becomes too lengthy.
2. LSTM (Long Short-Term Memory): LSTMs solve the long-term dependency issue with their gated cell structure, which selectively decides what information to remember or forget. When you type “Can we schedule a meeting,” an LSTM could better recall the earlier context even after you’ve added more words. This helps Smart Compose predict “for next week?” with higher accuracy.
3. GRU (Gated Recurrent Unit): GRUs, a simplified version of LSTMs, also manage long-term dependencies but with fewer gates, making them faster to train. In situations like writing quick emails, a GRU-based system could provide a real-time prediction with fewer resources while still offering reliable context memory.
4. Bidirectional RNN: In a task like understanding the full context of a sentence, Bidirectional RNNs excel by processing the sequence from both directions — forward and backward. This allows them to understand a word’s context based on both past and future inputs, making Smart Compose suggestions even more accurate.
5. Deep RNN: For more complex sentence structures or tasks that involve layered information, Deep RNNs, which stack multiple RNN layers, offer better predictions. These networks can learn more intricate patterns, much like how Smart Compose becomes more adept at handling nuanced phrases after being used frequently.

Limitations of Recurrent Neural Networks (RNNs)

While RNNs power impressive features like Smart Compose, they also come with a few limitations:

• Difficulty in Capturing Long-Term Dependencies: Basic RNNs often struggle to retain information over long sequences, which can result in inaccurate predictions for longer sentences. For example, remembering the beginning of a lengthy email thread may be challenging for standard RNNs.
• Vanishing and Exploding Gradients: During training, RNNs can face issues with vanishing or exploding gradients, where the weights either become too small or too large. This leads to ineffective learning, particularly in deeper networks with long sequences.
• Slow Training Time: The looping mechanism that makes RNNs powerful also makes them computationally expensive. Each step in a sequence relies on the previous one, which can lead to slow training times, especially when dealing with large datasets like email suggestions.
• Limited Parallelization: Since RNNs process data sequentially, they are less efficient at parallel computation, unlike other neural networks like Convolutional Neural Networks (CNNs), which can handle parallel processing. This can make RNNs slower when working with large volumes of data.
• Bias Towards Recent Data: RNNs tend to prioritize recent inputs over earlier ones, which means they may not always consider older information effectively. In language models, this can lead to suggestions that don’t always align with the overall context of a conversation.

RNNs After Transformers: How a Revolutionary Architecture Changed the Game in NLP

In recent years, the transformer algorithm has emerged as a game-changer in the field of natural language processing (NLP), surpassing traditional RNNs in many ways. Unlike RNNs, which process data sequentially — one word at a time — the transformer architecture allows for the entire sequence of words to be processed simultaneously. This parallelism provides a significant speed advantage over RNNs, which require sequential steps to capture dependencies in the data.

With RNNs, the primary challenge has always been retaining information from earlier parts of a sequence, especially in long sentences or texts. While LSTMs and GRUs addressed some of these limitations, they still rely on step-by-step processing, which makes it hard to fully capture long-range dependencies efficiently. Transformers, on the other hand, completely eliminate this sequential dependency. By using a mechanism called “self-attention,” transformers are able to weigh the importance of different words in a sentence regardless of their position. This allows them to understand context more effectively and capture relationships between words, even those far apart in a sequence.

In comparison, while RNNs perform well on short sequences and small-scale tasks, transformers excel at large-scale applications, such as language translation, text generation, and summarization. For example, in something like Smart Compose, a transformer-based model would be faster and more accurate at predicting your next words because it can instantly consider the entire sentence without needing to process each word in order.

In essence, transformers brought about a paradigm shift, allowing NLP models to understand and generate text more efficiently and effectively than ever before. While RNNs paved the way, transformers have set a new standard for how machines process language, creating more powerful and versatile models for real-world applications like Smart Compose and beyond.

Final Words

Recurrent Neural Networks (RNNs) have played a significant role in shaping how machines process sequential data, powering intelligent features like Smart Compose. While they have their limitations, especially with long-term dependencies, advancements like LSTMs and GRUs have enhanced their capabilities. However, with the rise of transformer models, a new era of efficiency and accuracy has emerged, revolutionizing the field of natural language processing. As we move forward, the strengths of both architectures can continue to inspire more advanced, context-aware systems for real-world applications.

That’s a wrap! I’m eager to hear your thoughts, feedback, or suggestions. Your input plays a huge role in helping me refine my ideas and expand my knowledge. Don’t hesitate to share your comments here or reach out to me directly on LinkedIn — https://www.linkedin.com/in/umesh-kumawat/.