The first step in calculating self-attention is to create three vectors from each of the encoder’s input vectors (in this case, the embedding of each word). Let’s first look at how to calculate self-attention using vectors, then proceed to look at how it’s actually implemented – using matrices. Self-attention is the method the Transformer uses to bake the “understanding” of other relevant words into the one we’re currently processing.Īs we are encoding the word "it" in encoder #5 (the top encoder in the stack), part of the attention mechanism was focusing on "The Animal", and baked a part of its representation into the encoding of "it".īe sure to check out the Tensor2Tensor notebook where you can load a Transformer model, and examine it using this interactive visualization. If you’re familiar with RNNs, think of how maintaining a hidden state allows an RNN to incorporate its representation of previous words/vectors it has processed with the current one it’s processing. When the model is processing the word “it”, self-attention allows it to associate “it” with “animal”.Īs the model processes each word (each position in the input sequence), self attention allows it to look at other positions in the input sequence for clues that can help lead to a better encoding for this word. What does “it” in this sentence refer to? Is it referring to the street or to the animal? It’s a simple question to a human, but not as simple to an algorithm. ” The animal didn't cross the street because it was too tired” Say the following sentence is an input sentence we want to translate: I had personally never came across the concept until reading the Attention is All You Need paper. Then, they each pass through a feed-forward neural network - the exact same network with each vector flowing through it separately.ĭon’t be fooled by me throwing around the word “self-attention” like it’s a concept everyone should be familiar with. The word at each position passes through a self-attention process. In this post, we will attempt to oversimplify things a bit and introduce the concepts one by one to hopefully make it easier to understand to people without in-depth knowledge of the subject matter.Ģ020 Update: I’ve created a “Narrated Transformer” video which is a gentler approach to the topic: Harvard’s NLP group created a guide annotating the paper with PyTorch implementation. A TensorFlow implementation of it is available as a part of the Tensor2Tensor package. The Transformer was proposed in the paper Attention is All You Need. So let’s try to break the model apart and look at how it functions. It is in fact Google Cloud’s recommendation to use The Transformer as a reference model to use their Cloud TPU offering. The biggest benefit, however, comes from how The Transformer lends itself to parallelization. The Transformer outperforms the Google Neural Machine Translation model in specific tasks. In this post, we will look at The Transformer – a model that uses attention to boost the speed with which these models can be trained. Attention is a concept that helped improve the performance of neural machine translation applications. In the previous post, we looked at Attention – a ubiquitous method in modern deep learning models. Watch: MIT’s Deep Learning State of the Art lecture referencing this post Translations: Arabic, Chinese (Simplified) 1, Chinese (Simplified) 2, French 1, French 2, Japanese, Korean, Persian, Russian, Spanish 1, Spanish 2, Vietnamese An applicative perspective of these different techniques is also presented, with a particular emphasis on the use of modern software tools.Hacker News (65 points, 4 comments), Reddit r/MachineLearning (29 points, 3 comments) The course discusses various approaches to learning and seeks to explain their basic mechanisms. These methods are characterized by a training phase using data or experiments to perform tasks that would be difficult or impossible to do using more conventional algorithmic means. Synopsis: This course focuses on methods for making inferences from observations of classification, regression, data analysis or decision making models. Introduction à l’apprentissage automatique - GIF-4101 / GIF-7005
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