1. Front
  2. Dedication
  3. Foreward
  4. Preface
  8. I. Structure and Intepretation of Tensor Programs
  9. Elements of Learning
    1. Types to Tensors
    2. Probabilities as Tensors
    3. Linear Models and their Algebra
    4. Non-Linear Models and their Stochastic Optimizers
  11. Anatomy of an Autograd
    1. Forwards
    2. Backwards
    3. Double Backwards
    4. Mutation
    5. Streams
  16. II. Compilation of Tensor Programs
  17. Serial Compilation
    1. Parsing llm.c (gpt2) to AST
    2. CFG-SSA + Linear Scan
    3. SoN + Graph Coloring
  19. Parallel Compilation
  20. Differentiable Compilation
  23. Afterword
  24. Bibliography
  25. Index