- Preface
- Notation and Conventions
- Part I: Foundations
- Sequence Modelling and the Two Paradigms
- Mathematical Preliminaries
- The Sequential Paradigm: Recurrent Networks
- Learning in Recurrent Networks: Backpropagation Through Time
- Gated Recurrence: LSTM and GRU
- Part II: The Parallel Paradigm
- Attention: From Alignment to Self-Attention
- The Transformer Architecture
- Positional Encoding: Recovering Order Without Recurrence
- Training the Parallel Model
- Part III: Comparative Analysis
- Computation, Parallelism, and Complexity
- Expressivity, Memory, and Long-Range Dependencies
- Hardware, Systems, and Throughput
- Empirical Comparison and Scaling Behaviour
- Part IV: Synthesis and Outlook
- Hybrids and Successors: The Return of Recurrence
- Choosing an Architecture: A Decision Framework
- Synthesis, Case Studies, and Outlook
- Back Matter
- Appendix A: Key Derivations
- Glossary of Key Terms
- References
- About This Book
- About the Author
The Sequence Machines: Comparing RNN and Transformer Architectures in Modern AI
This book is 100% completeLast updated on 2026-07-22
Two architectures. One choice: process a sequence step by step, or all at once. From that single decision flows almost every difference between RNNs and Transformers: how fast they train, how far they remember, and why parallelism won. A rigorous, diagram-rich comparison that derives the field instead of just surveying it.
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About the Book
The Sequence Machines is a graduate-level comparison of the two neural network architectures that have defined modern AI: recurrent neural networks (RNNs) and Transformers. It is built around a single thesis, that almost every practical difference between them follows from one design choice, whether information moves through a sequence step by step or all at once. Rather than survey the field, the book derives it, building both architectures from first principles and showing exactly where and why each one breaks. (The full multi-paragraph version and the "What's inside" list from my earlier message read the same, just with commas in place of the dashes.)
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About the Author
Louis Chukwunonye Nwadike
Louis Chukwunonye Nwadike is a researcher and writer working at the intersection of machine learning, applied statistics, and the engineering of large-scale systems. His interests include the mathematical foundations of deep learning, distributed training systems, and neural sequence architectures, along with the broader question of how complex algorithms can be explained clearly to a non-specialist audience. He has taught and advised in multiple settings on neural network theory and practice for several years.
He has a personal commitment to making rigorous machine learning literature accessible, especially to students from regions where access to expensive textbooks and elite seminar rooms is not the default. When he is not writing or working with code, he reads outside his field on purpose, in the belief that ideas travel between disciplines more easily than people often expect.
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