Neural Networks: Difference between revisions
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This can be expressed as<br> | This can be expressed as<br> | ||
[[File:Nn3.png|500px]]<br> | [[File:Nn3.png|500px]]<br> | ||
So I was expecting to somewhere define what to look for to be certain. E.g. the edges, lack of any pixels etc. But the video went of to speak about training and spoke of the cost function and taking the worst examples and squaring the results. To be honest I will need to buck up my linear maths to make some sense of what was going on. But knowing what you don't know is a start | So I was expecting to somewhere define what to look for to be certain. E.g. the edges, lack of any pixels etc. But the video went of to speak about training and spoke of the cost function and taking the worst examples and squaring the results. To be honest I will need to buck up my linear maths to make some sense of what was going on. But knowing what you don't know is a start<br> | ||
[[File:Nn4.png|500px]]<br> | [[File:Nn4.png|500px]]<br> | ||
Revision as of 22:05, 17 January 2025
Introduction
It is 2025 and time for me to spend more effort in understanding AI a bit more. So I will start, hopefully, at the beginning
What are Neural Networks
Watching the YouTube [here], it showed an example network which would be used to identify a digit in a 28 x 28 pixel picture. The video talks about about breaking the pixels up and passing them through layers. The layers determine a value for the thing you are looking for which comprises of a meaurement + weight + bias
For each neuron in one layer the result of the thing you are looking is calculated along with the weight and bias
This can be expressed as
So I was expecting to somewhere define what to look for to be certain. E.g. the edges, lack of any pixels etc. But the video went of to speak about training and spoke of the cost function and taking the worst examples and squaring the results. To be honest I will need to buck up my linear maths to make some sense of what was going on. But knowing what you don't know is a start
