Ever wondered how your phone suggests the next word in a sentence? Or how Google answers complex queries? Let's unwrap this mystery together!
Large Language Models (LLMs) are like the Shakespeare of the machine world. They're a type of Machine Learning (ML) model specially designed to understand and generate text.
Before machines can work their magic, they need to understand our language. And how do they do it? By converting text into numbers.
Python Example: Using NumPy for Vectorization
To simplify the text, LLMs break it into smaller chunks termed 'tokens'. This process, called Tokenization, is akin to dissecting a sentence word by word.
Python Example: Tokenizing Text Using Regular Expressions
Now, imagine LLMs having a dictionary. But instead of every word under the sun, they focus on the most frequently used ones to keep things efficient.
Python Example: Building a Vocabulary with Python
Each token (or word) in our machine's vocabulary gets a unique number, making it easily identifiable.
Python Example: Mapping Tokens to IDs
Now, the real magic! LLMs have various ways to represent these tokens. One popular method is "Embeddings", where words are mapped to points in space, capturing their essence.
Python Example: Using Embeddings with TensorFlow
Today, we journeyed through:
Stay curious and keep exploring! Whether you're a newbie or a seasoned techie, there's always more to learn in the ever-evolving world of AI.