Transformers One
Transformers One: The Ultimate Guide to Understanding Transformers
Introduction
Transformers are a type of machine learning model that has become increasingly popular in recent years. They are used in a wide variety of applications, from natural language processing to computer vision. In this guide, we will provide a comprehensive overview of transformers, including their architecture, training, and applications.
Architecture
Transformers are based on the encoder-decoder architecture. The encoder converts the input sequence into a fixed-length vector. The decoder then uses this vector to generate the output sequence.
The encoder and decoder are both composed of multiple layers. Each layer consists of a self-attention mechanism and a feed-forward network.
The self-attention mechanism allows the model to attend to different parts of the input sequence. This is important because it allows the model to capture long-range dependencies.
The feed-forward network is a simple neural network that is used to process the output of the self-attention mechanism.
Training
Transformers are typically trained using a supervised learning approach. The model is given a dataset of input-output pairs. The model then learns to map the input sequence to the output sequence.
The training process is typically divided into two stages. In the first stage, the model is trained to reconstruct the input sequence. In the second stage, the model is trained to generate the output sequence.
The training process can be computationally expensive. However, there are a number of techniques that can be used to speed up training.
Applications
Transformers have a wide range of applications, including:
Transformers have also been used to develop new models for a variety of other tasks, such as:
Transformers are a powerful tool that can be used to solve a wide range of problems. As the field of machine learning continues to develop, we can expect to see even more applications for transformers.
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