Row 4203

Row ID: 4203 | Dataset Entry | Axioma AXP Content Repository

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Hi everyone! I've been reading a lot into how ANN works but I do have a couple of questions. So let's say we want the program to recognize pictures of cats.

We have:

Input node - Pictures of cats in thousands of different situations, poses, colors, and different features. It recognizes the color and lightness values of every single pixel found in these pictures and recognizes patterns that are usually found in pictures with cats. (Our minds do this subconsciously just by living and seeing certain things more than once)

Weights - Every input is given a value that determines its strength and therefore "weight" that it carries when making the following calculations. So the placement and the color of that pixels has more or less weight in determining a feature. (This could be akin to us putting together certain features that help us recognize something. Retractable claws - Carnivore teeth, It meows - It has four legs) | How does the training work?

Hidden layers - Processing of data via mathematical calculations. Given those inputs, Claws + Feline sounds + Teeth + Quadrupedal =

Output - Cat

Is it in the hidden layers where the program picks from other trained models to recognize the features? Model for claws, teeth, and animal sounds.

I may have many more questions but I can't ask them without having answers to the previous :D

FieldValue
text Hi everyone! I've been reading a lot into how ANN works but I do have a couple of questions. So let's say we want the program to recognize pictures of cats. We have: Input node - Pictures of cats in thousands of different situations, poses, colors, and different features. It recognizes the color and lightness values of every single pixel found in these pictures and recognizes patterns that are usually found in pictures with cats. (Our minds do this subconsciously just by living and seeing c…
label r/neuralnetworks
dataType post
communityName r/neuralnetworks
datetime 2024-04-04
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Raw Record

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  "text": "Hi everyone! I've been reading a lot into how ANN works but I do have a couple of questions. So let's say we want the program to recognize pictures of cats.\n\n  \nWe have:\n\nInput node - Pictures of cats in thousands of different situations, poses, colors, and different features. It recognizes the color and lightness values of every single pixel found in these pictures and recognizes patterns that are usually found in pictures with cats. (Our minds do this subconsciously just by living and seeing certain things more than once)  \n\n\nWeights - Every input is given a value that determines its strength and therefore \"weight\" that it carries when making the following calculations. So the placement and the color of that pixels has more or less weight in determining a feature. (This could be akin to us putting together certain features that help us recognize something. Retractable claws - Carnivore teeth, It meows - It has four legs) | How does the training work?\n\nHidden layers - Processing of data via mathematical calculations. Given those inputs, Claws + Feline sounds + Teeth + Quadrupedal =  \n\n\nOutput - Cat   \n\n\nIs it in the hidden layers where the program picks from other trained models to recognize the features?  Model for claws, teeth, and animal sounds.  \n\n\nI may have many more questions but I can't ask them without having answers to the previous :D   \n\n\n  \n",
  "label": "r/neuralnetworks",
  "dataType": "post",
  "communityName": "r/neuralnetworks",
  "datetime": "2024-04-04",
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