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The validation loss settles exactly at an error of one. Probably means there's something off with either the kind of data validation set has or with something in the training. An exact validation loss of one almost definitely means there's something off. I'd recommend before doing anything thoroughly go through your data or see if there's anything to debug ...


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Depends on what does 1 represent in your task. If you are trying to predict household prices and 1 represents \$1, I think the average validation loss is good. If 1 represents \$10000 in this case, probably something is not right. But remember that there are 2 parts contributing to the overall loss. The mse loss and the l2 penalty loss. (Also remember that ...


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I found someone that has done this thing! You can hear a good explanation in Marcus Hutter's answer to this question about rewards given to AIXI. He describes a work that seems to be referring to this paper: Universal Knowledge-Seeking Agents for Stochastic Environments I'll edit this answer later with a full explanation of the approach, but essentially ...


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$f(x) = x^2 + b$ is a polynomial (more precisely, a parabola) so it is continuous, thus, a neural network (with at least one hidden layer) should be able to approximate that function (given the universal approximation theorem). After a very quick look at your code, I noticed you aren't using an activation function for your dense layers (i.e. your activation ...


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Intuitively, this is similar to the case when you are making predictions but you don't have all the necessary information to make the most accurate prediction or maybe there isn't a single accurate prediction, so you have a set of possible predictions (rather than a single prediction). For example, if you hadn't seen the last Liverpool game (in the ...


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The telltale signature of overfitting is when your validation loss starts increasing, while your training loss continues decreasing, i.e.: (Image adapted from Wikipedia entry on overfitting) It is clear that this does not happen in your diagram, hence your model does not overfit. A difference between a training and a validation score by itself does not ...


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One approach for this is collaborative filtering. see also link This does however need you to have data about some user preferences on some products. Given that you have stated you are willing to mine user preferences this approach may be feasible. The idea is that with this data you can train a model to predict how a user might rate a product. This is ...


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There are two factors that will change the ability of a deep neural network to fit a given dataset: either you need more data, or a deeper and wider network. Since the pattern is only 2-d, it can likely be approximated by some sort of simple periodic function. A DNN can approximate periodic functions pretty well, so the issue is probably that you don't have ...


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Welcome to AI.SE @EdouardLopez! Because Boston Dynamics is a private, for profit, company, we cannot know for sure how they achieve their results. However, we can examine the available public information and make educated guesses. In the information posted with the video, Boston Dynamics tells us that they use ... an optimization algorithm transforms ...


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