Bye-Bye RNNs, Hello Transformers: Why We Upgraded!

Recurrent Neural Networks (RNNs) face similar challenges:

1. Vanishing or Exploding Gradients:

Example: Translating a complex sentence like "The king, who ruled with an iron fist, was eventually overthrown by the people." The RNN might struggle to remember the king's "iron fist" by the time it reaches "overthrown," leading to an inaccurate translation.

2. Sequential Processing:

Example: Translating "Although the weather was bad, they went for a walk." The RNN might not understand the connection between "bad weather" and "went for a walk" until it reaches the end, leading to a confusing translation.

3. Limited Parallelism:

Example: Training an RNN on a massive dataset of books might take much longer compared to a Transformer, delaying your access to the translated knowledge.

Transformers

Imagine translating the simple sentence "I am a student" into French. Let's see how a Transformer model does it, focusing on key components:

Understanding the English (Encoder):

Input English Sentence: "I am a student"

1. Words to Numbers (Input Embedding): Each English word ("I," "am," "a," "student") becomes a numerical vector, capturing its meaning and context.

2. Word Order Matters (Positional Encoding) : The model adds information about each word's position in the sentence (e.g., "I" is first, "student" is last).

3. Word Relationships (Self Attention, Mult Head Attention & Feed Forward) : Each word "attends" to others, understanding how they connect and contribute to the overall meaning. Imagine "student" attending to "am" to confirm a singular form.

Generating the French (Decoder):

1. French Word Probabilities (Output Embedding) : The model predicts the next French word based on the encoded English and previously generated French words (e.g., "Je").

2. French Word Order (Positional Encoding) : Similar to the English, the model tracks the position of each generated French word (e.g., "Je" is first).

3. Context Matters (Masked Multi Head Attention) : The decoder only considers already generated French words and the encoded English, not peeking at future French words. This ensures it builds the sentence grammatically and logically.

4. More than Grammar (Multi Head, Feed Forward & Add Norm) : The model analyzes the generated context ("Je") to understand the meaning it needs to convey (e.g., existence or statement).

5. Choosing the Best Word (Linear & Softmax): Based on all the information, the model assigns probabilities to each possible French word ("suis," "parle," "fais"). "Suis" emerges as the most likely next word.

Final Output French Translation: "Je suis étudiant."

This is a simplified explanation, but it captures the essence of how Transformers work in machine translation.