# What is the correct way to read and analyse images in machine learning?

I am trying to understand the best practice to read and analyze images. If your image has 10,000 pixels, your input layers will have 10,000 inputs?

It sounds that my neural network will have too many inputs if I do it that way. Is that a problem? What is the recommended way of feeding an image through a neural network?

• A fully-connected network would actually have $10000 \times N^1$ weights where $N^1$ is the number of neurons in the first hidden layer. Assuming your CNN example is expanding number of channels from 3 to 32, its 288 weights when fully converted into a $3 \times 10000$ input feeding a $32 \times 10000$ first hidden layer, would have 9.6 billion weights to train! – Neil Slater Aug 10 '19 at 9:00
• edited to incorporate the $10000 \times N^1$. @NeilSlater how do you get 9.6 billion? – bjschoenfeld Aug 14 '19 at 19:23