Neural networks, in the simplest terms, are a type of computer system inspired by the human brain. They're designed to "learn" from information they process, in a way similar to how we learn from experience.
Here's a simple way to understand how they work:
- Input Layer: This is where the network receives information, just like our senses do. For example, if the neural network is learning to identify pictures of cats, the input would be the pixels of those images.
- Hidden Layer(s): These are the "thought processes" of the network. The information from the input layer gets processed here. The hidden layers contain nodes or "neurons" that apply specific calculations to the input data. Each neuron gives a certain weight or importance to its inputs, which can be adjusted during learning.
- Output Layer: This is the conclusion or decision made by the network based on its "thought processes". In our cat picture example, the output might be "this is a cat" or "this is not a cat."
Now, let's talk about the three most popular types of neural networks used in large language models:
- Recurrent Neural Networks (RNNs): RNNs are great for dealing with sequences of data, like sentences in a language model. They have "memory" because the output from one step is included in the next step. This allows them to take into account the order of words in a sentence, making them good at understanding language.
- Convolutional Neural Networks (CNNs): CNNs excel in processing grid-like data such as an image (which is essentially a grid of pixels) and are commonly used in image recognition tasks. However, they are also used in language processing tasks where they can capture local dependencies in the text by applying filters.
- Transformers: Transformers are currently the most popular method for large language models (like GPT-3). They use a mechanism called "attention" to weigh the importance of different words in understanding the meaning of a sentence. For example, in the sentence "The cat chased its tail", the word "chased" is important because it tells us what action is happening.
Each of these types of neural networks has its strengths and is used for different kinds of tasks. For example, RNNs are great for tasks that require memory of past events, like predicting the next word in a sentence. CNNs are fantastic at image recognition, and Transformers are currently state-of-the-art for many language tasks.