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One Neuron on its own can only solve linearly separable problems. You need a combination of Neurons to solve non-linearly separable problems. For the XOR case, you need at least 2 Neuron at the first layer, and 1 Neuron at the Output layer to properly classify it. Keep in mind sometimes the 3 Neuron network might get stuck in a local minima as well, you will ...


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Let's first recap the definition of the binary cross-entropy (BCE) and the categorical cross-entropy (CCE). Here's the BCE (equation 4.90 from this book) $$-\sum_{n=1}^{N}\left( t_{n} \ln y_{n}+\left(1-t_{n}\right) \ln \left(1-y_{n}\right)\right) \label{1}\tag{1},$$ where $t_{n} \in\{0,1\}$ is the target $y_n \in [0, 1]$ is the prediction (as produced by ...


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