Topic 1: The Bottleneck of Sequential Models
Topic: Topic 1: The Bottleneck of Sequential Models
Content adapted from Attention Is All You Need by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, Illia Polosukhin.Original Source
Topic 1 The Bottleneck Of Sequential Models Introduction
Break the sequential bottleneck! Compare RNN O(n) constraints with Transformer parallelization. Analyze hardware efficiency and the shift to self-attention.
RNN Foundations: Recurrence, State, and O(n) Bottlenecks
Master the math of RNNs, LSTMs, and GRUs. Understand hidden state updates, the O(n) sequential bottleneck, hardware constraints, and the limitations of BPTT.
Analyzing RNN Bottlenecks and Multi-Head Attention
Explore RNN sequential bottlenecks, path length complexity, and how Multi-Head Attention solves the resolution trade-off for scalable deep learning models.
Topic 1 The Bottleneck Of Sequential Models Guided Practice
Master the shift from RNNs to Transformers. Explore O(1) path lengths, Big-O complexity trade-offs, and hardware-constrained model architecture design.