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You could use scikit-learn's MultiLabelBinarizer. It's essentially the multi-label equivalent of one-hot encoding. For each movie, create a vector of zeros, where each zero is associated with a particular actor. If an actor is in that movie, change their zero to a one. In the context of a neural network, think of it as each actor having their own input ...


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From what I understood, you want to be able to determine whether the input to your classifier is a valid picture or not. Where: Valid picture: image of a person wearing or not wearing a seatbelt Not valid picture: unrelated images (say a kitchen picture) or noise, or a black image (no input at all) For that you could build a Bayesian model from your ...


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While your question has some ambiguities, I try to answer. From my understanding you want your model to predict “topic” of a sentence or a description. It’s just a classification problem with huge possible number of output classes. The first initial issue is very short length of documents (sentences). Most of topic modelling algorithms such as LDA have ...


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Assuming that you have access to the training data set, you could use an autoencoder network to predict what features f4, f5, f6 'could be' for the test data set. The way to do this is to train the autoencoder on the training data set with features f1, f2, f3 as inputs, and then use f1,f2,f3,f4,f5,f6 as the output of the network. The autoencoder then ...


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I assume you trained your model on (f1, f2, f3, f4, f5, f6) and in your test data you sometimes have (f1, f2, f3) and sometimes have for example (f1, f2, f3, f4, f5, f6), right? Because if your test data always have (f1, f2, f3), then isn't it better to just train a model on available features? So if my assumption is correct what I would do is to manipulate ...


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In general, it is definetely very computationally expensive, so the exhaustive search is not perfromed in pratice, however, there are some recent approaches for determining, whether the architecture is fine, without performing the training - by looking at the covariance matrix after forwarding the data, for example, in a recent paper - https://arxiv.org/abs/...


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Data management and bandwidth are key issues for interconnecting multiple GPUs. These are such big issues that it is hard to think about other challenges like neural network architecture, metrics, etc. The key to success for interconnecting multiple GPUs on a single computer is NVIDIA's NVLink: NVLink is a wire-based communications protocol for near-range ...


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I don't think people generally do use neural nets for grid world. As long as the state and action spaces are small enough, you should be able to store Q values in a table like you suggested. Neural nets come in handy when the state space is very large (or even continuous), so you can't afford to store a table of Q values. Also, neural nets have the ability ...


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Maybe LabelImg is what you are looking for? LabelImg is a graphical image annotation tool and label object bounding boxes in images. If not, maybe you can find other options for your problem on this summary of computer vision tools.


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