Making Sense of PyTorch

What I ended up discovering:

What I thought neural networks were like: Round #1

What I thought neural networks were like: Round #2

What I think neural networks are like today: Round #3

  • There a network of nodes and connections in there but it doesn’t appear in the code, similar to how there are packets and TCP/IP but you won’t see much of that in frontend JavaScript.
  • A great deal of work can be encapsulated in pre-trained networks and weights (such as ResNet). For a basic image labeler around that module I need to know only a linear transform and something like softmax or argsort.

Understanding inputs as tensors

Using a GPU

AutoML and Pre-Trained Models vs. From Scratch

Which config and options should I choose?

  • Including the optimizer in the learned parameters:
  • Removing BatchNorm without sacrificing quality:

Evaluation

import matplotlib.pyplot as plt
from sklearn.metrics import ConfusionMatrixDisplay
ConfusionMatrixDisplay.from_predictions(
y_true,
y_predicted,
display_labels=list_of_col_names
)

Stuff that still is kinda sketchy (for researchers, not just newbies)

Transformers vs. Doing ResNet Better

Normalizing and Augmenting

Adversarial Examples

Understanding vector-space

Robustness and drift

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Web->ML developer and mapmaker.

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Nick Doiron

Nick Doiron

Web->ML developer and mapmaker.

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