Since Feb 2022 I’ve been writing our textbook on Deep Learning with an Energy perspective. It will come in two versions: an electronic one with a dark background for screens (freely available) and a physical one with white background for printers (for purchase).

I finished writing the first 3 chapters and corresponding Jupyter Notebooks:

• Intro;
• Spiral;
• Ellipse.

Once the 4th chapter and notebook are done (end of Aug?), the draft will be submitted to the reviewers (Mikael Henaff and Yann LeCun). After merging their contributions (end of Sep?), a first draft of the book will be available to the public on this website.

## Book format

The book is highly illustrated using $\LaTeX$’s packages TikZ and PGFPlots. The figures are numerically generated with the computations done in Python using the PyTorch library. The output of such computations are stored as ASCII files and then read by $\LaTeX$ that visualises them. Moreover, most figures are also rendered on the Notebook using the Matplotlib library.

### Why plotting with $\LaTeX$?

Because I can control every single aspect of what is drawn. If I define the hidden vector $\green{\vect{h}} \in \green{\mathcal{H}}$ in the book, I can have a pair of axis lebelled $\green{h_1}$ and $\green{h_2}$ and the Cartesian plane labelled $\green{\mathcal{H}}$ without going (too) crazy. All my maths macros, symbols, font, font size, and colour are just controlled by one single stylesheet called maths-preamble.tex.

### Why colours

Because I think in colours. Hence, I write in colours. And if you’ve been my student, you already know that at the bottom left we’ll have a pink-bold-ex $\pink{\vect{x}}$ from which we may want to predict a blue-bold-why $\blue{\vect{y}}$ and there may be lurking an orange-bold-zed $\orange{\vect{z}}$.

## Illustrations sneak peeks

To keep myself motivated and avoid going crazy too much, I post the most painful drawings on Twitter, where my followers keep me sane by sending copious amount of love ❤️. You can find here a few of these tweets.

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Last update: 26 Jul 2022.

## Oct 2022 update

For the entire month of Aug and half of Sep I got stuck on implementing a working sparse coding algo for a low-dimensional toy example. Nothing was working for a long while, although I managed to get the expected result (see tweets below). Then, I spent a couple of weeks on the new semester’s lectures, creating new content (slides below, video available soon) on back-propagation, which I’ve never taught at NYU, topic that will make it to the book. Anyhow, now I’m back to writing! 🤓

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Last update: 26 Sep 2022.

## May 2023 update

Oh boy, this 4th chapter took me while (mostly because I’ve focussed also on other things, including the Spring 2023 edition of the course)… but it’s done now! In these last few months I’ve wrote about undercomplete autoencoders (AE), denoising AE, variational AE, contractive AE, and generative adversarial nets. Thanks to Gabriel Peyré, I’ve developed a method to separate stationary sinks and sources for a dynamics field (which I may write an article about), and it’s integral part of the book explanations.

Moreover, I’ve been pushing a few videos from the Fall 2022 edition of the course, which give a preview on the chapters I’ve been writing, e.g. neural nets components, backpropagation (first time teaching it), energy-based classification, PyTorch training, K-means, and sparse coding (at least for now). Finally, over the Winter break, I’ve been teaching 12 years-olds about the maths and computer science behind generative AI, and I’m considering using p5.js as a tool to teach programming to beginners.

What’s next? I’m sending this first draft, with its 4 chapters (Intro, Spiral, Ellipse, Generative) and companion Jupyter Notebooks to Yann for a review. Meanwhile, I’ll be writing down the Backprop chapter, possibly and article, and pushing a few more videos on YouTube. Once the review is completed, a first draft will pop to this website for the public.

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### Videos from DLFL22

Last update: 16 May 2023.

## Aug 2023 update

Of course, during the Summer it was unrealistic expecting anyone to review anything… Anyhow, I’ve just got back from O‘ahu (ICML23) and Maui (2 days before Lahaina burnt down) and finished the Backprop chapter, therefore the first draft will have 5 chapters in total as of right now. Below you can see a few diagrams I’ve developed over these Summer months.

The new semester starts in two weeks, so I’ll be a bit busy with that. I need to plan a possible chapter on joint embedding methods and start working on PART II of the book: ‘geometric stuff’.

About books, I’ve just received my copy of The Little Book of Deep Learning by François Fleuret. I have to say it is really well made and I really like it. It’s a bit on the terse side, but I haven’t decided if it’s a pro or a con.

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Last update: 16 Aug 2023.