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What You Need Before Transformers

Transformers are complex architectures. Build up these prerequisites to understand attention mechanisms and modern NLP.

8-12 weeks to prepare

9 topics to cover

4 essential

Core concepts you must understand first

4 topics

matrices

Attention is computed via matrix multiplications (Q, K, V)

Transformers are built from neural network layers

gradient descent

Training transformers requires optimization

probability basics

Softmax attention produces probability distributions

Key concepts for understanding how transformers work

3 topics

eigenvalues

Understanding attention heads and their learned patterns

rnn

Transformers were designed to replace RNNs - understand what they improve

softmax

Attention weights use softmax normalization

For deeper theoretical understanding

2 topics

taylor series

Positional encodings use Fourier-like expansions

distributions

Understanding layer normalization and output distributions

Once you've covered the essential prerequisites, you'll have a solid foundation for understanding transformers.