<p>Nassim answer is believed to be True for small networks and datasets but recent <a href="https://arxiv.org/pdf/1706.10239.pdf" rel="nofollow noreferrer">articles</a> (or e.g. <a href="https://arxiv.org/abs/1412.0233" rel="nofollow noreferrer">this</a> one) makes us believe that for deeper networks (with more than 4 layers) - not shuffling your data set might be considered as some kind of regularization - as poor minima are expected to be deep but small and good minima are expected to be wide and hard to leave. </p>

<p>In case of inference time - the only way where this might harm your inference process is when you are using a training distribution of your data in a highly coupled manner - e.g. using <code>BatchNormalization</code> or <code>Dropout</code> like in a training phase (this is sometimes used for some kinds of Bayesian Deep Learning).</p>
Do trained weights depend on the order in which trained data has been input?
<p>Suppose one makes a neural network using Keras. Do the trained weights depend on the order in which the training data has been fed into the system ? Is it ok to feed data belonging to one category first and then data belonging to another category or should they be random?</p>