 Five methods to convert language to vectors. Part two. One, convolution neural networks. These can be time-delay neural networks, which use convolution to process sequential data, and initially these were used in phoneme detection, but they can also be used in dynamic convolution neural networks. These are convolution networks whose pooling operation depends on the size of the sentence. Two, recurrent neural networks. These are feedforward neural networks rolled out over time, as such they can process sequence data like language. Three, transformer neural networks. These are encoder-decoder architectures that use attention to learn long-term dependencies. Four, BERT. These are a stack of transformer encoders to learn complexities of language. Five, GPT. These are a stack of transformer decoders to learn the complexities of language, and it's also the engine to chat GPT. Subscribe to the channel for more information.