Transformer Neural Networks

Introduction

• Lecture by Josh Starmer from StatQuest.
• Focus on explaining Transformer Neural Networks, specifically on translating English sentences into Spanish.

Neural Networks and Word Embedding

• Transformers are built on neural networks which process numbers as input values.
• Word Embedding: Converts words into numbers using a simple neural network.
  • Input for each word and symbol in the vocabulary is called a token.
  • Example words: let's, go, EOS (end of sequence).
  • Process: Multiply input values by weights to obtain output values (e.g., 1.87 for let's).
  • Identity Functions used initially, preserving the input values.
• Back Propagation: Optimizes weights by iteratively finding optimal values.

Positional Encoding

• Importance: Keeps track of word order in a sentence.
• Adds word order values to embeddings using alternating sine and cosine values.
  • Example: First word gets values from squiggle graphs (e.g., green, orange, blue).
  • Each word ends up with unique sequence of position values.

Self-Attention Mechanism

• Establishes relationships among words within a sentence.
• Process:
  • Calculate similarity scores among words (including similarity to itself).
  • Use Softmax Function to scale similarity values between 0 and 1.
  • Scale values: percentage of input word used to encode another word.
  • Calculate Query, Key, and Value for each word, reusing weights.
• Calculations allow parallel processing and quicker computations.

Encoder and Decoder Structure

Encoder

• Converts input sentences into encoded vectors.
• Components: Word Embedding, Positional Encoding, Self-Attention, Residual Connections.

Decoder

• Translates encoded vectors into output language.
• Process:
  • Start with embedding values for EOS token or SOS (Start of Sequence).
  • Add positional encoding (same sine and cosine squiggles).
  • Apply self-attention and residual connections.
  • Encoder-Decoder Attention: Calculates relationships between input and output.
  • Uses fully connected layers & final softmax to choose output token.

Practical Example

• Example translation: Let's go to Vamos.
  • Process EOS token, create embeddings, add positional encoding, calculate self-attention, and encoder-decoder attention.
  • Output sequence is determined by recurrent application of decoding steps until an EOS token is produced.

Additional Considerations

• Larger vocabularies require normalization after each step.
• Similarity calculations can vary; original Transformers use dot-product divided by √embeddings.
• Addition of extra layers for complex data handling.

Conclusion

• Transformers efficiently encode input and translate into output using advanced neural network techniques.
• Supports parallel processing, making it effective for large datasets.

Additional Resources

• Check out StatQuest PDF study guides and book for more on statistics and machine learning statquest.org.
• Support options: Patreon, channel membership, merchandise.