Introduction to Transformers

1. What are Transformers?

Transformers are a type of neural network architecture introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. They are designed to handle sequential data for tasks such as language translation and text summarization.

2. Key Components

• Encoder-Decoder Structure
• Self-Attention Mechanism
• Positional Encoding

3. Attention Mechanism

The self-attention mechanism allows the model to weigh the importance of different words in the input sequence, enabling it to focus on specific words based on the context.

# Basic Self-Attention Calculation
import numpy as np

def self_attention(Q, K, V):
    scores = np.dot(Q, K.T) / np.sqrt(K.shape[-1])
    weights = softmax(scores)
    output = np.dot(weights, V)
    return output
        

4. Advantages of Transformers

• Parallelizable Training
• Long-Range Dependencies Handling
• Scalability

5. Applications

Transformers have a wide range of applications including:

• Natural Language Processing
• Image Processing
• Speech Recognition

6. Code Example

Here's a simple implementation using PyTorch:

import torch
import torch.nn as nn

class TransformerModel(nn.Module):
    def __init__(self, input_dim, model_dim, num_heads):
        super(TransformerModel, self).__init__()
        self.attention = nn.MultiheadAttention(embed_dim=model_dim, num_heads=num_heads)
        self.fc = nn.Linear(model_dim, input_dim)

    def forward(self, x):
        attention_output, _ = self.attention(x, x, x)
        return self.fc(attention_output)
        

7. FAQ