# Definition A neural network is composed of the interconnection of fundamental units called **neurons**. Every **neuron** uses the input values received and process this values to generate an output. Then, every neuron has the following inputs represented by: $ X = \{x_1, x_2, \dots, x_n\} $ Every input is pondered by the weight values defined by: $ W^i = \{w_1^i, w_2^i, \dots, w_n^i\} $ We should interpret the value $w_j^i$ as the weight or importance of the input value $x_j$ that reaches neuron $i$ from neuron $j$. We use an [[Input or combination function]]. And we apply an [[Activation function]] to generate a new output named $y_i$. ![[Pasted image 20240902104838.png]] --- ## References - [[Deep learning - Anna Bosch Rué Jordi Casas Roma Toni Lozano Bagén]] - [[DeepLearningPrincipios.pdf]]