Transformer原理与代码精讲（PyTorch）
Develop essential data science & ai skills with expert instruction and practical examples.
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Transformer发轫于NLP（自然语言处理），并跨界应用到CV（计算机视觉）领域。目前已成为深度学习的新范式，影响力和应用前景巨大。本课程对Transformer的原理和PyTorch代码进行精讲，来帮助大家掌握其详细原理和具体实现。原理精讲部分包括：注意力机制和自注意力机制、Transformer的架构概述、Encoder的多头注意力（Multi-Head Attention）、Encoder的位置编码（Positional Encoding）、残差链接、层规范化（Layer Normalization）、FFN（Feed Forward Network）、Transformer的训练及性能、Transformer的机器翻译工作流程。代码精讲部分使用Jupyter Notebook对Transformer的PyTorch代码进行逐行解读，包括：安装PyTorch、Transformer的Encoder代码解读、Transformer的Decoder代码解读、Transformer的超参设置代码解读、Transformer的训练示例（人为随机数据）代码解读、Transformer的训练示例(德语-英语机器翻译)代码解读。.
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