Row 3710

Row ID: 3710 | Dataset Entry | Axioma AXP Content Repository

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I am following [this](https://www.kaggle.com/code/ancientaxe/simple-neural-network-from-scratch-in-python/notebook) simple tutorial, but subbing in [this](https://huggingface.co/datasets/pirocheto/phishing-url) dataset and adapting accordingly.

I've got it working (most of the time), and the network can predict whether the URLs are phishing or legitimate with ~95% accuracy (when it works).

Occasionally, maybe 1 in 5 times, the weights produced end up being totally useless for prediction. Is this a common issue with neural network training? I can provide code if desired, but the code is nearly identical to what's in the tutorial, except for some changes to the number of input and output neurons and obviously the different treatment of different data.

It's like it hickups somewhere in the beginning of the training process, and can't escape the calamity it's found itself in.

Has anyone encountered something similar in their work? I'm super new to all this, so apologies if this question is naive.

FieldValue
text I am following [this](https://www.kaggle.com/code/ancientaxe/simple-neural-network-from-scratch-in-python/notebook) simple tutorial, but subbing in [this](https://huggingface.co/datasets/pirocheto/phishing-url) dataset and adapting accordingly. I've got it working (most of the time), and the network can predict whether the URLs are phishing or legitimate with ~95% accuracy (when it works). Occasionally, maybe 1 in 5 times, the weights produced end up being totally useless for prediction. …
label r/neuralnetworks
dataType post
communityName r/neuralnetworks
datetime 2024-03-21
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{
  "text": "I am following [this](https://www.kaggle.com/code/ancientaxe/simple-neural-network-from-scratch-in-python/notebook) simple tutorial, but subbing in [this](https://huggingface.co/datasets/pirocheto/phishing-url) dataset and adapting accordingly.  \n\nI've got it working (most of the time), and the network can predict whether the URLs are phishing or legitimate with ~95% accuracy (when it works).  \n\nOccasionally, maybe 1 in 5 times, the weights produced end up being totally useless for prediction.  Is this a common issue with neural network training?  I can provide code if desired, but the code is nearly identical to what's in the tutorial, except for some changes to the number of input and output neurons and obviously the different treatment of different data.\n\nIt's like it hickups somewhere in the beginning of the training process, and can't escape the calamity it's found itself in.  \n\nHas anyone encountered something similar in their work?  I'm super new to all this, so apologies if this question is naive.",
  "label": "r/neuralnetworks",
  "dataType": "post",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-03-21",
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}
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