# Is learning process of artificial neural networks natural?

I am starting with machine learning and I am currently trying to understand artificial neural networks. In a multi-layer perceptron one minimizes some cost function (it can e.g. cross-entropy), but I have to feed the neural network with all the training data I have. This is not what a typical human learning process looks like. Rather, the experience changes by providing new examples through time and the prediction may change due to novel informations. In other words, the machine/net has to response to the changing environment by refining its predictions. I have two questions:

• Isn't it more natural to use Bayesian techniques for optimization where the next training example improves the experience (my neural net will improve through time by providing a new data)?

• Does a neural net needs all the training data from the start or can I keep improving it by providing a new information (e.g. after some time) and without referring to the previous training data (I don't have to optimize everything once again but with larger collection of data)?

• For neural networks you need to train the old information alongside with the new information, otherwise your network will converge to only the new information and not the old information. But this depends on how much you train your network the new information (e.g. if your information is 10% of the total size of the information, and you only train it 100 epochs with learning rate 0.01 it isn't always necessary to train the old information). – Thomas W May 4 '17 at 11:02
• All machine learning models improve with more data, not just Bayesian ones. They can also be trained "online", as the data arrives. There are debates about biologically accurate artificial neural networks, but your question is more generic. – Emre May 4 '17 at 15:54

• What I wanted to say is that when you have a cost function $J(\theta)$ that you can split into pieces e.g. $J(\theta) = \sum\limits_{i=1}^m J_{i}(\theta)$ and you will minimize each piece separately then it is not necessarily the minimum of the whole thing $J(\theta)$. The parameters $\theta$ that you will find will only minimize the last part $J_{m}(\theta)$. Maybe the technique you described by taking random batches and re-runing everything many times truly find the minimum of the whole thing - like you are getting towards equilibrium. I should read more about this stuff. – Bociek May 4 '17 at 14:16