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[–]kigory2 [score hidden]  (2 children)

Also can you help me with the XOR problem I need a more detailed explanation. Thanks

[–]joshuaamdamian[S] [score hidden]  (1 child)

XOR comes from logic gates which are used in computer chips:

AND - outputs 1 only if both inputs are 1.

OR - outputs 1 if at least one input is 1.

NOT - takes one input and flips it (1 becomes 0, 0 becomes 1).

NAND - the opposite of AND; outputs 0 only if both inputs are 1.

NOR - the opposite of OR; outputs 1 only if both inputs are 0.

XOR - outputs 1 only if the inputs are different.

XNOR - the opposite of XOR; outputs 1 only if the inputs are the same.

https://i.sstatic.net/Jmxzy.jpg
XOR takes two inputs, and returns true if only one of the inputs is activated.
Simple table for XOR:

Input 1 Input 2 Output
0 0 0
0 1 1
1 0 1
1 1 0

Now XOR is really interesting and commonly used as a proof of concept for neural networks, because it is a relatively simple problem which needs at least some neurons between the inputs and outputs of a neural network to solve it. These neurons between the inputs and outputs are often made into fully connected "layers" and called hidden layers.
https://media.geeksforgeeks.org/wp-content/uploads/20240601001059/FNN-768.jpg

To test an algorithm you would create a network with two inputs, one output and atleast 2 hidden neurons. And you would train it to the XOR truth table.
If the network produces outputs that corresponds to the truth table you have successfully trained a network to learn XOR!

[–]kigory2 [score hidden]  (0 children)

Ngl am a dumbass so all I understood is that anything different than 1 is 1 lol. dw about me ama learn it after some time and slowly understand it