Description: The Softmax function is a mathematical function that transforms a vector of real values into a probability distribution. This function is particularly relevant in the context of neural networks, where it is commonly used in the output layer for multi-class classification problems. The Softmax function takes a vector of inputs and applies the exponential function to each of its elements, normalizing these values so that the sum of all resulting probabilities equals one. This allows the results of the neural network to be interpreted as probabilities, facilitating decision-making in classification tasks. One of the key features of the Softmax function is that it highlights the differences between input values; that is, a higher value in the input vector will result in a significantly higher probability in the output, helping the network make more accurate predictions. Additionally, the Softmax function is differentiable, allowing its use in training neural networks through backpropagation techniques. In summary, the Softmax function is fundamental for converting the outputs of a neural network into interpretable probabilities, making it an essential tool in the fields of machine learning and artificial intelligence.
