Overall, the experience was pretty good. I found the elixir-nx libraries rather stable in terms of APIs even if they are pre 1.0. And when I had doubts or when I stumbled across some potential issues, I’ve reached out for help in Github or here in the forum.
Nice one Nick! Do you think (with your work) it’s a good book for Elixir users? Is it an easy read? If so maybe we should run a book club on it
Paolo Perrotta is a great author - he wrote one of my favourite Ruby books: Metaprpogramming Ruby! Here’s my recollection of it in his spotlight on DT:
Do you think (with your work) it’s a good book for Elixir users? Is it an easy read?
It is definitely a good and well written book, with a practical approach and only a couple of “heavy-math” chapters. In general I liked it and I found it easy to follow, even if I didn’t have much experience with numpy. In the first chapters I struggled a bit to replicate the examples in Nx, but mainly because I was completely new to the library and to the concepts, that’s part of the game.
If so maybe we should run a book club on it
I think it can be a valuable reading if you wanna start with ML, on the other hand, the book is 3 years old and given the pace of innovation in ML, maybe there are more up to date resources out there The basis won’t change tho’, but for example I’d have like some chapters on more advanced topics such as NLP, transformers, reinforcement learning (but I guess they deserve a book on their own ).
Paolo Perrotta is a great author - he wrote one of my favourite Ruby books: Metaprpogramming Ruby!
I never read it, but I saw a talk on Git by him once and it was great
I’ve tried the Dockyard tutorials at first since I didn’t want to leave Elixir-land but soon found it easier to understand nx/axon it after I understood the analogous libraries in Python.
Be happy to provide feedback on the livebooks as I progress.
Welcome @shawn_leong ! And thanks for the kind words, really appreciated!
found it easier to understand nx/axon it after I understood the analogous libraries in Python.
Indeed, knowing a bit of numpy and Keras (which are used in the book) can be really helpful to understand Nx/Axon and navigate their APIs.
I didn’t have any experience in ML before reading the book and i never used these python libraries as well (numpy yes, but for other stuff), therefore in some cases I needed to figure out which API to use/compose together the reach the same result illustrated in the book, especially in the first chapters. For instance, my first implementation to initialize the weights with zeroes was:
# Given n elements it returns a tensor
# with this shape {n, 1}, each element
# initialized to 0
defp init_weight(x) do
n_elements = elem(Nx.shape(x), 1)
Nx.tile(Nx.tensor([0]), [n_elements, 1])
end
Then, later on I switched to this one which I believe is more correct:
# Given n elements it returns a tensor
# with this shape {n, 1}, each element
# initialized to 0
defnp init_weight(x) do
Nx.broadcast(Nx.tensor([0]), {elem(Nx.shape(x), 1), 1})
end
Be happy to provide feedback on the livebooks as I progress.
GitHub - nickgnd/programming-machine-learning-livebooks: Programming Machine Learning - Elixir Livebooks
The basis won’t change tho’, but for example I’d have like some chapters on more advanced topics such as NLP, transformers, reinforcement learning (but I guess they deserve a book on their own 
).
