!rant

PUBLIC SERVICE ANNOUNCEMENT:

For AI, in particular Deep Learning developers, practitioners, hobbyists and otherwise people interested in the field.

If you go into the Pytorch website, click on resources and scroll down you will see a link to "Deep Learning with Pytorch" by Manning publications. This will give you access to the book, a book that if memory serves me well costs about 40+ in printing and the online book format is about 29 (again, if memory serves well)

The book is currently FREE and it does not ask you for an email address, you can just tell them why you want it for and they will give you the free pdf download.

I don't know how good the book is, but have found Manning to publish really good resources.

Do with this information what you want.

And yes, I am leaving the rant tag, so that more people can see this and take advantage of the opportunity in case of being interested and not having the money to purchase the book after the promotion is done and over with. Fuck you about tags and shit.

python machine learning tutorials:
- import preprocessed dataset in perfect format specially crafted to match the model instead of reading from file like an actual real life would work
- use images data for recurrent neural network and see no problem
- use Conv1D for 2d input data like images
- use two letter variable names that only tutorial creator knows what they mean.
- do 10 data transformation in 1 line with no explanation of what is going on
- just enter these magic words
- okey guys thanks for watching make sure to hit that subscribe button

ehh, the machine learning ecosystem is burning pile of shit let me give you some examples:

- thanks to years of object oriented programming research and most wonderful abstractions we have "loss.backward()" which have no apparent connection to model but it affects the model, good to know
- cannot install the python packages because python must be >= 3.9 and at the same time < 3.9
- runtime error with bullshit cryptic message
- python having no data types but pytorch forces you to specify float32
- lets throw away the module name of a function with these simple tricks:
"import torch.nn.functional as F"
"import torch_geometric.transforms as T"
- tensor.detach().cpu().numpy() ???
- class NeuralNetwork(torch.nn.Module):
def __init__(self):
super(NeuralNetwork, self).__init__() ????
- lets call a function that switches on the tracking of math operations on tensors "model.train()" instead of something more indicative of the function actual effect like "model.set_mode_to_train()"
- what the fuck is ".iloc" ?
- solving environment -/- brings back memories when you could make a breakfast while the computer was turning on
- hey lets choose the slowest, most sloppy and inconsistent language ever created for high performance computing task called "data sCieNcE". but.. but. you can use numpy! I DONT GIVE A SHIT about numpy why don't you motherfuckers create a language that is inherently performant instead of calling some convoluted c++ library that requires 10s of dependencies? Why don't you create a package management system that works without me having to try random bullshit for 3 hours???
- lets set as industry standard a jupyter notebook which is not git compatible and have either 2 second latency of tab completion, no tab completion, no documentation on hover or useless documentation on hover, no way to easily redo the changes, no autosave, no error highlighting and possibility to use variable defined in a cell below in the cell above it
- lets use inconsistent variable names like "read_csv" and "isfile"
- lets pass a boolean variable as a string "true"
- lets contribute to tech enabled authoritarianism and create a face recognition and object detection models that china uses to destroy uyghur minority
- lets create a license plate computer vision system that will help government surveillance everyone, guys what a great idea

I don't want to deal with this bullshit language, bullshit ecosystem and bullshit unethical tech anymore.

Oh yeah Google why don't you just change the parameter order of functions, remove entire functions between minor versions, and not put a single example on your API docs? And force devs to add 30 lines of boilerplate and start an http server so I can run the debugger? Fuck tensor flow, I'm moving to pytorch.

ohhhhh I am pissseddddddddddd

itss the fucking pytorch.module class it would seem !

I do exactly the same goddamn shit as its supposed to do in a goddamn notebook and run it step by step and the fucking model trains and the output values change !!!! and the loss decreases !!

I do this in the goddamn class derived from model with a call to model.parameters() and the fucker fails !!!

why ???
why ?????
why ??????
is it cloning the goddamn parameters so the references aren't there ????
seems to work goddamn fine when i call a layer and activation function at a goddamn time chaining the calls one after another !!!!!!

UGHHHH IT LOOKS LIKE IF YOU DEFINE THE LOSS AND OPTIMIZER OUTSIDE THE FUCKING CLASS IN A SEPERATE TRAINING FUNCTION IT DOESN'T TRAIN !!!!!!

WHY ??
A REFERENCE IS A GODDAMN REFERENCE !!!!