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Why Activation Functions?
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Classroom Contents
Transformer Encoder in 100 Lines of Code
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- 1 What we will cover
- 2 Introducing Colab
- 3 Word Embeddings and d_model
- 4 What are Attention heads?
- 5 What is Dropout?
- 6 Why batch data?
- 7 How to sentences into the transformer?
- 8 Why feed forward layers in transformer?
- 9 Why Repeating Encoder layers?
- 10 The “Encoder” Class, nn.Module, nn.Sequential
- 11 The “EncoderLayer” Class
- 12 What is Attention: Query, Key, Value vectors
- 13 What is Attention: Matrix Transpose in PyTorch
- 14 What is Attention: Scaling
- 15 What is Attention: Masking
- 16 What is Attention: Softmax
- 17 What is Attention: Value Tensors
- 18 CRUX OF VIDEO: “MultiHeadAttention” Class
- 19 Returning the flow back to “EncoderLayer” Class
- 20 Layer Normalization
- 21 Returning the flow back to “EncoderLayer” Class
- 22 Feed Forward Layers
- 23 Why Activation Functions?
- 24 Finish the Flow of Encoder
- 25 Conclusion & Decoder for next video
