docs: i18n: add zh-CN machine translation (#385)

subtitles/zh-CN/04_the-transformer-architecture.srt

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