1. Introduction to Transformers

Transformers are a groundbreaking type of neural network architecture introduced in 2017. Unlike older models like RNNs or LSTMs, which process data sequentially (word by word), Transformers analyze entire sequences of data (e.g., sentences, images) all at once. This parallel processing makes them faster and more efficient, especially for long texts.

Why Transformers Are Better Than RNNs/LSTMs

• Parallel Processing: Imagine reading a sentence one word at a time vs. seeing the whole sentence instantly. Transformers do the latter, allowing them to train faster on modern hardware like GPUs.
• Long-Range Relationships: Traditional models struggle with connections between distant words (e.g., "The cat, which chased the mouse all afternoon, finally caught it"). Transformers effortlessly link "cat" and "it" using attention.
• Scalability: Models like GPT-3 and BERT use Transformers to handle massive datasets, learning complex patterns in text, images, and more.

2. Transformer Architecture

The Transformer has two main parts: the encoder (understands input) and the decoder (generates output). Let’s break down how they work:

Key Components

1. Input Embeddings
   • Words are converted into numerical vectors (like coordinates on a map). For example, "apple" becomes [0.2, -0.5, 0.7, ...], capturing its meaning.
   • These vectors are learned during training, so similar words (e.g., "dog" and "puppy") end up close to each other.
2. Positional Encoding
   • Since Transformers process all words at once, they need a way to encode word order.
   • Imagine adding a unique "position tag" to each word. For example:
     • "I [position 1] love [position 2] pizza [position 3]."
   • These tags use sine and cosine waves to represent positions mathematically.
3. Self-Attention Mechanism
   • The core innovation of Transformers.
   • Each word "asks" questions about other words:
     • "Which words are relevant to me?"
     • "How much should I focus on each word?"
   • Example: In "The animal didn’t cross the street because it was too tired," the model uses attention to link "it" to "animal."
4. Multi-Head Attention
   • Think of this as having multiple "teams" of attention mechanisms.
   • Each team focuses on different relationships (e.g., one team tracks subject-verb connections, another analyzes pronouns).
   • Results from all teams are combined to create a rich representation of the text.
5. Feedforward Network
   • After attention, each word’s representation passes through a small neural network to refine its meaning.
6. Layer Normalization & Residual Connections
   • These stabilize training by ensuring data doesn’t "explode" or "vanish" as it moves through layers.
   • Residual connections let the model "remember" earlier versions of a word’s meaning.

Encoder vs. Decoder

• Encoder: Processes input (e.g., a sentence in French) into a context-rich representation.
• Decoder: Uses the encoder’s output to generate the target sequence (e.g., the English translation).
• The decoder also uses masked attention to prevent cheating (e.g., when predicting the next word, it can’t peek at future words).