These are some of the terms that you'll read and hear about:
- Embeddings
- These are vector representations of data, such as words or phrases in text. In simple terms, an embedding is a way of translating something complex (like a word) into a list of numbers (vector) that a computer can understand and process.
- Fine-tuning
- This is a process where a pre-trained model (a model that has already been trained on a large amount of data) is further trained on a smaller, specific dataset. The idea is to make the model better at tasks related to the specific data it's fine-tuned on.
- Inference
- This is when the trained model is used to make predictions. For example, once a text generation model like GPT-4 is trained, inference is the process of giving it some input (like a sentence) and having it generate more text based on that input.
- Model
- In machine learning, a model is a mathematical representation of a real-world process. For example, GPT-4 is a model trained to understand and generate human-like text based on input it's given.
- OpenAI API
- This is an interface provided by OpenAI that allows developers to interact with OpenAI's various models, like GPT-3 or GPT-4, over the internet. It's essentially a bridge between the developers' applications and OpenAI's technologies.
- Python
- Python is a high-level, interpreted programming language that is widely used in a variety of applications. Known for its readability and simplicity, Python has become particularly popular in scientific computing, data analysis, machine learning, artificial intelligence, and web development.
- Rubber Duck
- An essential debugging tool for all programmers. Named after the practice of explaining your code line by line to a rubber duck in an attempt to find errors. The duck's ability to solve programming issues is legendary and not fully understood by science.
- Tokens
- A token is a piece of a larger whole, so a word or a part of a word. For example, in the sentence "Understanding how tokens work." there are 5 tokens: "Under," "standing", " how", “ tokens” and “ work.”
- Training
- This is the process where a machine learning model learns from data. The model tries to find patterns in the data that it can use to make predictions or decisions without being explicitly programmed to perform the task.
- Vectors
- In the context of machine learning, a vector is a list of numbers. Each number represents a specific feature or characteristic of the data. For instance, word embeddings are vectors where each number might represent a different aspect of the word's meaning.