🔁 RNN, 🧠 LSTM, ⚡ GRU – Complete Overview
📌 Why are LSTM and GRU Needed?
Basic RNNs suffer from the vanishing gradient problem, where older information is lost over time. To solve this and preserve long-term memory, advanced structures like LSTM and GRU were developed.
🧠 LSTM Structure Summary
- Cell State: long-term memory store
- Forget Gate: decides what past information to discard
- Input Gate: decides what new information to remember
- Output Gate: decides what to send to the next time step
⚡ GRU Structure Summary
- Update Gate: controls memory retention
- Reset Gate: controls how much of the past to forget
- Uses only hidden state (no cell state) – simpler and faster
📊 Comparison Table
| Aspect | RNN | LSTM | GRU |
|---|---|---|---|
| Memory retention | Weak | Strong | Medium–Strong |
| Speed | Fast | Slow | Medium |
| Parameter count | Low | High | Medium |
| Use cases | Short sentiment analysis | Translation, speech, medical | Real-time prediction, chatbot |
📂 Python Examples (TensorFlow)
🔁 RNN
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import SimpleRNN, Dense
model = Sequential([
SimpleRNN(64, input_shape=(10, 1)),
Dense(1)
])🧠 LSTM
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
model = Sequential([
LSTM(64, input_shape=(10, 1)),
Dense(1)
])⚡ GRU
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import GRU, Dense
model = Sequential([
GRU(64, input_shape=(10, 1)),
Dense(1)
])🧠 Use Case Summary
| Model | Primary Applications | Description |
|---|---|---|
| 🔁 RNN | Sentence sentiment analysis, autocomplete | Good for short sequences, simple structure |
| 🧠 LSTM | Machine translation, speech recognition, medical time series | Excellent for long-term dependencies |
| ⚡ GRU | Real-time forecasting, chatbots, IoT | Lighter and faster than LSTM, ideal for mobile/web |
📄 Visual PDF Diagram
Download the visual comparison of LSTM vs GRU structures here:
📎 LSTM_GRU_Comparison_Diagram.pdf
Author: ChatGPT | Continuously updated with deep learning fundamentals to advanced use cases 🔄
