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. The first three chapters lay the ground: what the book is about, the linear algebra and calculus a network runs on, gradient descent, and what a dataset and a loss function are.
This is a sample: 3 of 13 chapters.
The third part of the book, Down the Rabbit Hole, continues our exploration of differentiable models by considering alternative designs for sets, most importantly attention layers and transformer models in Chapters 10 and 11, graphs in Chapter 12, and finally recurrent layers for temporal sequences in Chapter 13.
Vectors possess their own algebra, which we call a vector space, in the sense that any two vectors 𝐱 and 𝐲 of the same shape can be linearly combined, 𝐳 = a𝐱 + b𝐲, to provide a third vector, with zᵢ = axᵢ + byᵢ.
Geometrically, the derivative can be understood as the slope of the tangent passing through a point, or equivalently as the best first-order approximation of the function itself at that point, as shown in Figure F.2.1.
We can generalize this idea with the concept of convexity of a function. There are many possible definitions of convexity; we choose the one below for simplicity of exposition.
Fine-tuning is made especially easy by the presence of large open-source repositories online.
The overall beauty of this setup is marred by the fact that the posterior is in general impossible to compute in closed form, except for very specific choices of prior and likelihood. Lacking this, one is forced to approximate solutions, either by Markov chain Monte Carlo or by variational inference.
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
Upload a textbook or any difficult book. The first five minutes are free.
Upload a book