# 📚 PyTorch Tutorial for Beginners - Part 4/6: Sequence Modeling with RNNs, LSTMs & Attention
#PyTorch #DeepLearning #NLP #RNN #LSTM #Transformer

Welcome to Part 4 of our PyTorch series! This comprehensive lesson dives deep into sequence modeling, covering recurrent networks, attention mechanisms, and transformer architectures with practical implementations.

---

## 🔹 Introduction to Sequence Modeling
### Key Challenges with Sequences
1. Variable Length: Sequences can be arbitrarily long (sentences, time series)
2. Temporal Dependencies: Current output depends on previous inputs
3. Context Preservation: Need to maintain long-range relationships

### Comparison of Approaches
| Model Type | Pros | Cons | Typical Use Cases |
|------------------|---------------------------------------|---------------------------------------|---------------------------------|
| RNN | Simple, handles sequences | Struggles with long-term dependencies | Short time series, char-level NLP |
| LSTM | Better long-term memory | Computationally heavier | Machine translation, speech recognition |
| GRU | LSTM-like with fewer parameters | Still limited context | Medium-length sequences |
| Transformer | Parallel processing, global context | Memory intensive for long sequences | Modern NLP, any sequence task |

---

## 🔹 Recurrent Neural Networks (RNNs)
### 1. Basic RNN Architecture

class VanillaRNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super().__init__()
        self.hidden_size = hidden_size
        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x, hidden=None):
        # x shape: (batch, seq_len, input_size)
        out, hidden = self.rnn(x, hidden)
        # Only use last output for classification
        out = self.fc(out[:, -1, :])  
        return out

# Usage
rnn = VanillaRNN(input_size=10, hidden_size=20, output_size=5)
x = torch.randn(3, 15, 10)  # (batch=3, seq_len=15, input_size=10)
output = rnn(x)

### 2. The Vanishing Gradient Problem
RNNs struggle with long sequences due to:
- Repeated multiplication of small gradients through time
- Exponential decay of gradient information

Solutions:
- Gradient clipping
- Architectural changes (LSTM, GRU)
- Skip connections

---

## 🔹 Long Short-Term Memory (LSTM) Networks
### 1. LSTM Core Concepts

Key Components:
- Forget Gate: Decides what information to discard
- Input Gate: Updates cell state with new information
- Output Gate: Determines next hidden state

### 2. PyTorch Implementation

class LSTMModel(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super().__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, 
                           batch_first=True, dropout=0.2 if num_layers>1 else 0)
        self.fc = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        # Initialize hidden state and cell state
        h0 = torch.zeros(self.lstm.num_layers, x.size(0), 
                        self.lstm.hidden_size).to(x.device)
        c0 = torch.zeros_like(h0)
        
        out, (hn, cn) = self.lstm(x, (h0, c0))
        out = self.fc(out[:, -1, :])
        return out

# Bidirectional LSTM example
bidir_lstm = nn.LSTM(input_size=10, hidden_size=20, num_layers=2,
                    bidirectional=True, batch_first=True)

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