GRU Architecture

 GRU (Gated Recurrent Unit) 

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🧠 What is GRU?

GRU (Gated Recurrent Unit) is a type of Recurrent Neural Network (RNN) architecture introduced in 2014.
It was designed to solve the same problems as LSTM (Long Short-Term Memory) but with a simpler structure and fewer parameters.

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🚀 Why GRU?

• Handles long-term dependencies.
• Mitigates vanishing gradient problem.
• Simpler & faster than LSTM.
• Performs well on sequence data like text, time series, audio.

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🔑 Core Components of GRU:

At each time step t, GRU has:

1. Update Gate (z_t):
   Controls how much of the past information to keep.

2. Reset Gate (r_t):
   Controls how much of the past information to forget.

3. Candidate Activation (h̃_t):
   Computes new information to add.

4. Final Hidden State (h_t):
   Combines old state and new candidate info.

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⚙️ Mathematical Equations:

Given input x_t and previous hidden state h_{t-1}:

1. Update Gate (z_t):

z_t = σ(W_z * [h_{t-1}, x_t] + b_z)

Controls how much of the past to keep.

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2. Reset Gate (r_t):

r_t = σ(W_r * [h_{t-1}, x_t] + b_r)

Controls how much past info to forget.

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3. Candidate Activation (h̃_t):

h̃_t = tanh(W_h * [r_t * h_{t-1}, x_t] + b_h)

Uses reset gate → combines past + present info.

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4. Final Hidden State (h_t):

h_t = (1 - z_t) * h_{t-1} + z_t * h̃_t

Mixes old hidden state & new candidate.

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📊 GRU Architecture Diagram:

Simplified Visual:

Input x_t
   ↓
+--------------------+
|    Update Gate     | -----> z_t
+--------------------+
   ↓
+--------------------+
|    Reset Gate      | -----> r_t
+--------------------+
   ↓
+----------------------------------+
|  Candidate Activation (h̃_t)      |
| Combines reset gate & input      |
+----------------------------------+
   ↓
+----------------------------------+
| Final Hidden State (h_t)         |
| Combines old state & candidate   |
+----------------------------------+

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Detailed Visual Flow:

Previous Hidden State (h_{t-1}) ─────────┐
                                         │
Input (x_t) ─────────────┐               ▼
                         │         +-------------+
                         └────────▶ | Update Gate|──────▶ z_t
                                   +-------------+
                                         │
                                         ▼
                                  +-------------+
                                  | Reset Gate  |─────▶ r_t
                                  +-------------+
                                         │
                                         ▼
                                 ┌──────────────────┐
                                 │ Apply Reset Gate │
                                 └──────────────────┘
                                         │
                                         ▼
                                 +-------------------+
                                 | Candidate h̃_t     |
                                 +-------------------+
                                         │
                                         ▼
                         +-------------------------------------+
                         | Combine with h_{t-1} via Update Gate|
                         +-------------------------------------+
                                         │
                                         ▼
                                Final Hidden State (h_t)

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🟢 GRU vs LSTM:

Feature	GRU	LSTM
Gates	2 (Update, Reset)	3 (Input, Forget, Output)
Memory Cell	No separate cell state (uses hidden state)	Separate cell state and hidden state
Parameters	Fewer	More (heavier)
Computation Speed	Faster	Slightly slower
Performance	Similar (depends on dataset/task)	Sometimes better for very long sequences

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🌟 Key Benefits of GRU:

• Simpler architecture, fewer parameters.
• Efficient for training, faster convergence.
• Good balance between speed & performance.

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🚀 Applications of GRU:

• NLP (Language Modeling, Translation)
• Speech Recognition
• Time Series Forecasting
• Stock Market Prediction
• Video Data Analysis

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