Neural networks fudging the numbers
Why do we still need data scientists?
The whole purpose of supervised neural networks is to find the best parameters of a certain model that can successfully map some inputs to some outputs. Although neural networks can be impressive, even to the limit of being magical, there is really no magic behind the learning mechanism itself. It usually involves specifying a loss function that acts as a punishment metric and an optimizer that tries its best to reduce the losses. In a previous blog post, we explained all these concepts in details, the general mechanics of neural networks and the details of each of its specific gear.
Let’s say we are trying to predict the quantity of rain falling outside by checking the power of reception of a satellite signal. As we can expect, we usually have the strongest satellite signal on non-rainy days, and the worst signal under heavy rainy days. The goal of the neural network is to find the transfer function that maps satellite signal to the rain quantity.
Let’s say the first random initialization of the neural network created the model rain=2. Meaning no matter what the satellite reception power is, we’re…
