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Let's suppose that we have an MLP with $15$ inputs, $20$ hidden neurons and $2$ output neurons. The operations performed are only in the hidden and output neurons, given that the input neurons only represent the inputs (so they do not perform any operation). Each hidden neuron performs a linear combination of its inputs followed by the application of a non-...

3

What is the time complexity? The time complexity of an algorithm is the number of basic operations, such as multiplications and summations, that the algorithm performs. The time complexity is usually expressed as a function of the input's size $n$ (but this does not always have to be the case: for instance, you can express the time complexity as a function ...

2

One important point I missed in first review: error is a summatory, its derivative is also a summatory. About offsets "b": usually they are different in each cell (if not fixed to some value, as 0). Thus, replace them by b1 and b2. Moreover, they should be optimized in the same way that the weights.

2

The networks in NEAT are still implicitly layered. There are neurons that need to be evaluated before other neurons can be evaluated and so this gives us our layers. If you don't know the structure of your network then you can use Kahn's algorithm to find an arbitrary (by arbitrary I just mean one of the possible partially ordered sets) ordering of the ...

2

Language models produce a probability distribution over a set of words. You determine the next word by sampling from this distribution. So, determining the next word is stochastic even though the distribution is the same given the initial prompt.

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I don't think there's a "standard way" of expressing the forward pass: you use the transpose when you need to use it, and this depends on how you define the weights and inputs matrices, and on the architecture of your neural network. For example, in a fully connected feedforward neural network, you know that every neuron in the previous layer is ...

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