When diving into AI/ML, we constantly hear about transformers and their revolutionary impact. But how did we get here? The journey started from Traditional Neural Networks ➡ Recurrent Neural Networks (RNNs) ➡ Attention Mechanisms and finally to Transformers.
RNNs were a significant milestone because they addressed sequential data processing but had major limitations. These limitations led to the birth of attention mechanisms and transformers. Interestingly, before GPT became a household name, Google extensively used RNNs in their products, including predictive text, speech recognition, and early recommendation systems.
What is a Recurrent Neural Network (RNN)?
Have you ever wondered how your keyboard predicts the next word while typing? Before transformers took over, RNNs were the secret behind word suggestions and other sequential tasks.
Definition:
An RNN (Recurrent Neural Network) is a type of artificial neural network designed specifically for processing sequential data. Unlike traditional feed-forward networks, RNNs have loops that allow information to persist, making them well-suited for tasks where context and order matter.
Common Use Cases of RNNs:
👉 Natural Language Processing (NLP) (e.g., speech recognition, text generation, machine translation)
👉 Time Series Prediction (e.g., stock price forecasting, weather prediction)
👉 Sequence Modeling (e.g., handwriting recognition, music composition)
How Does an RNN Work?
Let’s use the word prediction example to understand the key stages of an RNN. When you type a sentence, the RNN processes it in three key stages:
1️⃣ Input Processing Stage
This is the data collection stage, where the model gathers input to make predictions.
🔹 When you first start using a social media app, its recommendations may seem random or inaccurate. Over time, as you interact more, the app starts to understand your preferences. This happens because it continuously collects data from you to make better predictions.
🔹 In the keyboard analogy, as you type more, the model stores your words in memory, helping it predict what you might type next.
2️⃣ Hidden State Update (Memory Stage)
At this stage, the RNN updates its hidden state based on the new input and previous memory.
🔹 In social media, the model remembers what you’ve previously engaged with and adjusts recommendations accordingly. If you keep watching travel vlogs, it will prioritize showing more similar content.
🔹 In keyboard predictions, the model updates its memory with each word you type, continuously refining its contextual understanding.
3️⃣ Output Generation Stage
Now, the model makes a decision and produces an output based on its learning.
🔹 In social media, this means recommending the next video or post that best matches your interests.
🔹 In typing, this means predicting and suggesting the next word in your sentence.
4️⃣ Backpropagation Through Time (BPTT) – Training Stage
This is where the model learns from past mistakes and improves over time. Think of it like a student reviewing mistakes from past exams to perform better in the next test.
🔹 In social media, if you suddenly stop engaging with travel vlogs and switch to fitness videos, the model realizes its past predictions were wrong and adjusts itself to reflect your new interests.
🔹 In keyboard predictions, if you frequently delete a suggested word and type something else, the model adapts to improve its future suggestions.
How Does BPTT Work?
1️⃣ The model compares its predictions to actual user behavior.
2️⃣ If there’s an error, it adjusts its internal weights to improve accuracy.
3️⃣ This process repeats across multiple iterations, constantly fine-tuning the model.
This stage is crucial because it ensures that the model evolves and adapts based on real-world interactions.
Final Thoughts
RNNs played a critical role in AI’s evolution, powering early NLP applications, recommendations, and predictive text systems. However, due to limitations like vanishing gradients and slow processing, they were eventually replaced by transformers and attention-based models.
Yet, understanding RNNs is essential because they paved the way for modern AI breakthroughs. Without RNNs, we wouldn’t have reached the transformer era of GPT! 🚀
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