Recurrent Neural Networks (RNNs) are used for processing sequential or time-series data by maintaining a hidden states that captures memory of past inputs within a sequence.
They have a feedback loop, meaning each output depends on the previous inputs — making them ideal for tasks like sentiment analysis or speech recognition.
However, RNNs face key challenges:
- Vanishing gradient problem – they struggle to learn long-term dependencies.
- Limited memory – as sequences get longer, they gradually forget earlier information.
Long Short-Term Memory (LSTM) networks come into the picture as an improved version of RNNs, designed to solve the problems of vanishing gradients and long-term dependency.
Unlike traditional RNNs, LSTMs have a memory cell that helps retain important information for longer durations — allowing them to "remember" context across longer sequences.
Architecture Overview:
- Forget Gate: Decides which part of the previous cell state to keep or forget.
- Input Gate: It is actually the one that adds new information into the memory cell.
- Output Gate: Controls what information is passed to the next step.
Real use case: Grammar correction tools like QuillBot, which rely on understanding long text dependencies to rephrase sentences accurately.
However LSTM face key challenges:
- Computationally Expensive
- Requires more memory.
LSTMs can’t analyze an entire sentence at once since they process information sequentially, step by step.
This is where Transformers come in — self-attention-based models designed for processing natural language efficiently. Unlike RNNs or LSTMs, Transformers process entire sentences in parallel, allowing them to capture context and relationships between words more effectively.
Examples: ChatGPT, BERT, GPT-4
Key Features:
- Capable of performing Sequence-to-Sequence tasks (e.g., language translation).
- Built using multiple encoder–decoder layers for deeper understanding.
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