（徽标呼啸而过） (logo whooshing)
- 让我们 Transformer 的架构。 - Let's study the transformer architecture.
该视频是编码器的介绍视频， This video is the introductory video to the encoders,
解码器和编码器 - 解码器系列视频。 decoders, and encoder-decoder series of videos.
在这个系列中， In this series,
我们将尝试了解是什么构成了 transformer 网络， we'll try to understand what makes a transformer network,
我们将尝试用简单、高层次的术语来解释它。 and we'll try to explain it in simple, high-level terms.
无需深入了解神经网络， No advanced understanding of neural networks is necessary,
但了解基本向量和张量可能会有所帮助。 but an understanding of basic vectors and tensors may help.
开始， To get started,
我们将处理这张图 we'll take up this diagram
从原来的变压器纸， from the original transformer paper,
Vaswani 等人题为 “注意力就是你所需要的”。 entitled "Attention Is All You Need", by Vaswani et al.
正如我们将在这里看到的， As we'll see here,
我们只能利用它的一部分， we can leverage only some parts of it,
根据我们正在尝试做的事情。 according to what we're trying to do.
我们想深入到特定的层次， We want to dive into the specific layers,
建立那个架构， building up that architecture,
但我们会尝试理解不同的方式 but we'll try to understand the different ways
可以使用此架构。 this architecture can be used.
让我们先开始 Let's first start
通过将该架构分成两部分。 by splitting that architecture into two parts.
在左边我们有编码器， On the left we have the encoder,
右边是解码器。 and on the right, the decoder.
这两个可以一起使用， These two can be used together,
但它们也可以独立使用。 but they can also be used independently.
让我们了解这些是如何工作的。 Let's understand how these work.
编码器接受表示文本的输入。 The encoder accepts inputs that represent text.
它转换这个文本，这些词， It converts this text, these words,
成数值表示。 into numerical representations.
这些数值表示 These numerical representations
也可以称为嵌入或特征。 can also be called embeddings, or features.
我们会看到它使用了 self-attention 机制 We'll see that it uses the self-attention mechanism
作为其主要组成部分。 as its main component.
我们建议你查看视频 We recommend you check out the video
关于编码器具体要了解 on encoders specifically to understand
这个数字表示是什么， what is this numerical representation,
以及它是如何工作的。 as well as how it works.
我们将更详细地研究自注意力机制， We'll study the self-attention mechanism in more detail,
以及它的双向属性。 as well as its bi-directional properties.
解码器类似于编码器。 The decoder is similar to the encoder.
它还可以接受文本输入。 It can also accept text inputs.
它使用与编码器类似的机制， It uses a similar mechanism as the encoder,
这也是掩蔽的自我关注。 which is the masked self-attention as well.
它不同于编码器 It differs from the encoder
由于其单向特性 due to its uni-directional feature
并且传统上以自回归方式使用。 and is traditionally used in an auto-regressive manner.
在这里，我们也建议你查看有关解码器的视频 Here too, we recommend you check out the video on decoders
特别是要了解所有这些是如何工作的。 especially to understand how all of this works.
结合两部分 Combining the two parts
结果就是所谓的编码器 - 解码器， results in what is known as an encoder-decoder,
或序列到序列转换器。 or a sequence-to-sequence transformer.
编码器接受输入 The encoder accepts inputs
并计算这些输入的高级表示。 and computes a high-level representation of those inputs.
然后将这些输出传递给解码器。 These outputs are then passed to the decoder.
解码器使用编码器的输出， The decoder uses the encoder's output,
与其他输入一起生成预测。 alongside other inputs to generate a prediction.
然后它预测输出， It then predicts an output,
它将在未来的迭代中重复使用， which it will re-use in future iterations,
因此，术语自回归。 hence the term, auto-regressive.
最后，为了理解 Finally, to get an understanding
编码器 - 解码器作为一个整体， of the encoder-decoders as a whole,
我们建议你查看有关编码器 - 解码器的视频。 we recommend you check out the video on encoder-decoders.
（徽标呼啸而过） (logo whooshing)