Alice's Adventures in a Differentiable Wonderland, Volume I: A Tour of the Land
The same text, told at every level, in every language and look shown here. Pick, then listen or download.
The same player the product gives for your own book: the voice, the drawings as they are drawn, the text.
A modern textbook on how neural networks really work, from tensors and gradients to transformers. Chapter 6 shows how a program finds its own gradients: automatic differentiation, the reverse walk behind every trained model.
This is a sample: 1 of 13 chapters.
The book's answer starts by looking at the model as a program. When the program runs, it performs a chain of small, simple operations, one feeding the next; the book calls each one a primitive, and the whole chain, as it actually ran, a computational graph.
Now the first road, forward mode. Think of it as walking along the chain in the direction the program runs, carrying a notebook.
The book's key observation is that the chain ends in one number, the total error. So if you start the multiplication from that end, what you carry is never a whole table, only a single row of numbers, and a row times a table is cheap and gives another row.
Two examples show why the answers are so much cheaper than the tables. For the most common piece of a network, multiplying the input by a table of weights, the full table of slopes with respect to the weights would be a huge three-dimensional block of numbers.
Object-style models, the usual way of writing PyTorch, need one more step. One option is to pull the weights out of the model object and call it as a plain function with those weights handed in.
The book adds a warning. For curves that bend both ways, subgradients need not exist, and mathematicians use a looser, local version instead.
Examples for: a curious adult with school maths who uses AI apps every day; add one modern instance per idea, such as how a chat assistant or a phone's photo app is trained
The lectures retell the English text of arXiv 2404.17625 (version 3, 2025) by Simone Scardapane, published under an open licence that allows retelling and translation. The retelling, the drawings and the voice are made by AI and checked against the book by a second model. How AI is used.
The lectures adapt the original and are released under the same licence, free to share and adapt with credit to the author and a link to this page: CC BY-SA 4.0
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