【谷歌生成式人工智能课程】[中英字幕]5.Encoder-Decoder Architecture Overview duketian 在家作AI,昨天780,一台电脑,操作简单,分享我的接单平台、接单技巧和资源分享,目前经济自由!! 我与zz 【硬核解析】从GPGPU到CUDA,看完秒懂GPU为何主宰AI !(GPU渲染管线工作原理大揭秘)!!
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而multi-head self-attention是Transformer的核心组成部分,和简单的attention不同之处在于,Multihead机制将输入拆分为许多小的chunks,然后并行计算每个子空间的scaled dot product,最后我们将所有的attention输出进行拼接。 Transformer,很多时候我们也称之为"vanilla Transformer", 它有一个encoder-decoder的结构,decoder的Tran...
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The architecture of vision transformers is heavily influenced by the original transformer architecture. Having a solid grasp of the transformer architecture, especially the encoder component, is crucial for comprehending vision transformers. This section provides an overview of the key elements of a transf...
1. weakness of simple RNNenc Architecture 传统的neural machine translation 大都是基于encoder-decoder模型,即通过一个encoder将源句子编码成一个固定长度的向量表示,再使用decoder将这个向量作为输入,然后获得输出。这样的话,Encoder需要将整个句子编码到一个固定长度的向量,如... ...
Transformer,很多时候我们也称之为"vanilla Transformer", 它有一个encoder-decoder的结构,decoder的Transformer可以在语言建模的时候获得非常好的效果。 从2020年,Transformer开始在CV领域大放异彩:图像分类,目标检测,语义分割,图像生成等。而从深度学习暴发以来,CNN一直是CV领域的主流模型,而且取得了很好的效果,相比之下...
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简单来讲就是Encoder作为编码器,用来获取输入数据的feature或者context(context vector一般是padding成固定的长度),然后输入到Decoder中解码得到输出。Seq2Seq的输入和输出都可以是任意长度。在机器翻译模型中,Encoder和Decoder一般都是Recurrent Neural Network;在应用到图像领域中时,Encoder一般采用CNN获取图像的feature,再用...
This bidirectional learning is made possible through the way that the framework is pre-trained with transformer-based architecture. What is a Transformer, and how does BERT use it? The Transformer is an encoder-decoder architecture by which BERT can better understand the contextual relationship of ...