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You don't have a full reinforcement learning problem, but appear to have a context-free k-armed bandit problem: The start state at time $t$ is essentially irrelevant to the problem. It does not impact available actions, reward or next state. The next state at time $t+1$ is only of interest because it determines the reward. All actions are effectively ...


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This was recently answered on the PyTorch Forums. Yes, torch.inference_mode is indeed preferable to torch.no_grad in all situations where inference mode does not throw a runtime error. The reason it took until version 1.9 to be implemented was precisely because it was originally difficult to ensure that all "unsafe" operations in inference mode ...


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The computer vision problem that you are describing is object detection, i.e. the problem of finding the location of specific objects in an image and label them correctly with their names. There are many resources on the web (or in books) that describe this problem more in detail and examples (which also include code) to get you started with it (e.g. this ...


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The first step You need to decide if you want to hold each string column or not. Then you must encode your text fields into numbers which you need to use some embedding algorithms like word2Vec. Check here. Second step Probably, you will have a lot of columns. Now, you need to reduce the dimension space. PCA, manifold transforms, partial least squares ...


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I don't think there is any rationale behind choosing 80/20 over 75/25 or others. But those are the numbers for rather small datasets. If your dataset is large enough (like hundreds of thousands of samples), you can even work with 98/1/1 percents for train/val/test as discussed by Andrew Ng in this video. Neural networks thrive with big data and it is always ...


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It seems your problem is more related to Face Identification than Face Recognition. I understand you are looking for the implementation using a NN based approach, but if you're open to giving it a try to other approaches you could consider using Eigenfaces, which is based in PCA. For that, you can find some references and code implementations. Datasets you ...


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It depends on what you consider to be fake news: if you want to say if a news story tells truth, or if you want to detect if a news story uses language that is typical for fake news. Probably the major criterion is that the content news story is in contradiction with the state of affairs. If this is your criterion of fakeness, you need to do some sort of ...


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This is a question of time series forecasting, since your numbers form a sequence. You may want to take a look at the "forecasting" tag at CrossValidated. If you have only 700 data points, ML/AI methods will likely not be very useful. Whatever you do, I would recommend you benchmark your chosen method against very simple approaches, like the ...


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Since you only have only 700 observations, I would not try a deep learning approach. I think it is very unlikely that any Deep Learning approach will learn a non-obvious relationship with that little data. What you could try is create a set of features based on lags. Create a feature, that is lagged by 1, by 2, by 3, and so on. Also moving average of lagged ...


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I guess the most "suitable" approach is to look up research papers on ML/AI/Stats based methods on bipolar disorder mood swings prediction/regression etc. Focus on the abstract, intro/related works and conclusion. Find out why the method is proposed, what the well-known approaches are, what the intuition for the proposed methods are. Find out the ...


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As all you have is a series of numbers, you should try using a sequence model. I suggest you look into RNNs and in particular LSTMs. Of course this is assuming despite the lack of "obvious patterns", there are some kind of hidden patterns in your data. If not, what you have is not very different than random walk in 3 dimensions - which makes the ...


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You might want to start on googling the "Irregular Cutting Stock Problem". I think your problem formulation is similar to Irregular Cutting Stock Problem. Some cool papers are up in the results such as this heuristic method which is tested on real-world based problem instances. By browsing the existing heuristic/metaheuristic methods, you may get ...


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do we also want to consider the subset of invalid actions for the $\max\limits_{a}Q(s_{t+1},a)$ No. Doing so would go against the theory behind the Bellman equation from which the update derives. The value of $r_{t+1} + \gamma \max\limits_{a'}Q(s_{t+1},a')$ needs to match to a realisable trajectory, otherwise the eventual expected values may be estimates ...


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You can always adjust class weights accordingly. I know the reference is not for image data but it shouldn't matter if you are doing classification. Here is another answer more direct to the point.


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you are kind of right. but no necessarily. domain randomization : when you widen the range of your training data parameters to make your model more generalized. this can be done for any purpose. (even if you are not doing domain adaptation). domain adaptation : when you train your model on data from a certain domain (lets say to detect cars) and then test it ...


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We have a vector $w*$ such that it has separated all of the data points. This implies if the correct classification of a point is -1, then $w^{*T}x_n$ is also negative. If $y_n$ is positive, then $w^{*T}x_n$ is positive. Thus is because we've stated every point is correctly classed. Thus $\rho>0$


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