A new DEV Community post argues that understanding Transformers requires mastering basic neural networks first. The author explains that Transformers are simply deep feedforward networks with attention layers added on top. Every block contains standard linear layers, activation functions, and gradient-based learning, making the architecture less intimidating once you strip away the attention mechanism.

The article breaks down core components like weights, biases, and activation functions. It highlights that models like BERT and GPT use GELU instead of ReLU for smoother gradient flow in deep networks. The post also covers forward and backward passes, showing how backpropagation works the same way in Transformers as in any standard neural network.

Ultimately, the piece positions feedforward networks as the real workhorses inside each Transformer block, handling the heavy lifting while attention simply mixes information. By demystifying these foundations, developers can better grasp how these models learn. The author promises the next post will cover embeddings, the crucial step that feeds attention its input vectors.