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+# Differentiation for Hackers
+
+The goal of this handbook is to demystify *algorithmic differentiation*, the
+tool that underlies modern machine learning. It begins with a calculus-101 style
+understanding and gradually extends this to build toy implementations of systems
+similar to PyTorch and TensorFlow. I have tried to clarify the relationships
+between every kind of differentiation I can think of – including forward and
+reverse, symbolic, numeric, tracing and source transformation. Where typical real-word ADs are mired in implementation details, these implementations are designed to be coherent enough that the real, fundamental differences – of which there are surprisingly few – become obvious.
+
+The intro notebook is recommended to start with, but otherwise notebooks do not have a fixed order.
+
+* [Intro](https://github.com/MikeInnes/diff-zoo/blob/notebooks/intro.ipynb) – explains the basics, beginning with a simple symbolic differentiation routine.
+* [Back & Forth](https://github.com/MikeInnes/diff-zoo/blob/notebooks/backandforth.ipynb) – discusses the difference between forward and reverse mode AD.
+* [Forward](https://github.com/MikeInnes/diff-zoo/blob/notebooks/forward.ipynb) – discusses forward-mode AD and its relationship to symbolic and numerical differentiation.
+* [Tracing](https://github.com/MikeInnes/diff-zoo/blob/notebooks/tracing.ipynb) – discusses tracing-based implementations of reverse mode, as used by TensorFlow and PyTorch.
+* [Reverse](https://github.com/MikeInnes/diff-zoo/blob/notebooks/reverse.ipynb) – discusses a more powerful reverse mode based on source transformation (not complete).
+
+If you want to run the notebooks locally, they can be built by running the
+`src/notebooks.jl` script using Julia. They should appear inside a `/notebooks`
+folder. Alternatively, you can run through the scripts in Juno.