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Maybe this is what you are looking for: https://en.wikipedia.org/wiki/Aho%E2%80%93Corasick_algorithm Basically you would build and store a finite-state machine that resembles a trie with additional links between the various internal nodes using the given keywords. for the candidate document, go through it with the above finite-state machine. Then based on ...


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I'll answer in a couple of stages. I feel somewhat lost as to what the input for the NN should look like. Your choices boil down to two options, each with their own multitude of variants: Vector Representation: Your input is a vector of the same size as your vocabulary where the elements represent the tokens in the input example. The most basic version of ...


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Your list is complete for what is considered 'popular' by most practitioners who apply AI for stock trading. Supervised learning and rule learning are at the top for accuracy. There are more academic papers published on classifiers than on regression approaches; classifiers are typically more accurate than regressors.


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If $D = \{ A, B \}$ is a dataset that contains both labelled and unlabelled data, where $A = \{ (x_i, y_i) \}_{i=1}^n$, $B = \{ x_i \}_{i=1}^m$, and $m \gg n$, then, to use self-supervised learning (for representation learning), you could follow these steps learn representations of your images $x_i$ by training a neural network $M$ with $B$ to solve a so-...


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What are you hoping to get out of the answer for this question? Feature scaling is a method you CAN(but don't have to) use, so that your algorithm performs faster and reaches better general accuracy. I would say that on a simple regression task, where the feature value ranges do not vary a lot, the output would probably be almost the same, but as soon as you ...


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I think you are looking for the field known as explainable artificial intelligence. The book Interpretable Machine Learning: A Guide for Making Black Box Models Explainable will surely help you to understand the issues and existing techniques. See also the following question Which explainable artificial intelligence techniques are there?.


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Some notes about VQ-VAE: In the paper, they used PixelCNN to learn the prior. PixelCNN is trained on images. The discrete latent variables are just the indices of the embedding vectors. For example, you can put your embedding vectors in an array. For a single input image, the number of the output channels of the encoder before quantization equals the ...


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Yes, you're interpreting the $\max$ there wrongly. In your second formula $$ \operatorname{Regret}_{T}(\mathcal{H})=\max _{h^{\star} \in \mathcal{H}} \operatorname{Regret}_{T}\left(h^{\star}\right) \label{1}\tag{1} $$ The sign $=$ means "is defined as", so maybe the following notation is less confusing $$ \operatorname{Regret}_{T}(\mathcal{H}) \...


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In general it's better to not use sigmoid function in any hidden layer. There are many other great options such as ReLU and ELU. However, if for any reason you have to use sigmoid-like function, then go with Tanh function, at least it has ~0 mean.


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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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The policy doesn't change over time. That is, the values will change, otherwise we would not be learning anything, but our rules for action selection don't. I.e. we always take action according to the distribution postulated to our current estimate of the policy $\pi_\theta(a|s)$, we don't suddenly start taking $\max_a \pi_\theta(a|s)$, which would be a true ...


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It depends, as mentioned in comments, on your model and labels. For example how would you use standardisation on multi classification problem? Generally, standardisation is more flavourable for input data as its mean is around 0. I assume you have a regression model and in that case using standardisation could be better than normalisation.


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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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How to fix the network above to auto-classify XOR data, in unsupervised manner? This cannot be done, except accidentally. Unsupervised learning cannot replace or emulate supervised learning. As a thought experiment, consider why you would expect the network to discover XOR, when simply considering outputs rounded to binary, you could equally find AND, OR, ...


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I'm not sure any intelligent mechanism can be entirely free of symbolic logic. Even where a decision is statistically based, a machine that takes actions must include some form of: IF {some condition} THEN {some action} As to the popularity of newly proven statistical AI methods (ANN and genetic algorithms), this derives from the greater utility they ...


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You might also ask if there's any particular reason why we would use a neural net. If we're to train a neural net to play chess, we need to be able to: 1. Feed it positions as input vectors (easy enough), 2. Decide on an output format. Perhaps a distribution over possible moves (but then, how to represent that such that the meaning of a specific output cell ...


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In reinforcement learning (RL), an immediate reward value must be returned after each action, alomng with the next state. This value can be zero though, which will have no direct impact on optimality or setting goals. Unless you are modifying the reward scheme to try and make an environment easier to learn (sometimes called reward shaping), then you should ...


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ANNs as used today need 1. a lot of data 2. a lot of computational power. Before we had any of the above two, we didn't really know how to properly build ANNs since we didn't quite have the means to train the network, and thus couldn't evaluate it. "Symbolic AI" on the other hand, is very much just a bunch of if-else/logical conditions, much like ...


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It's not a specific web for sell or share neural network models, but actually you can easily find other people models in Github! Just search it! For example, this is a random repo I've found for Cat Classification. But.. the problem is everyone have different problems. So you can't easily use other people neural network models and then use it for your ...


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You could say that the class of the data (e.g. spam vs not spam) is a hidden quality that can be inferred through the observable features (e.g. message subject contains "bitcoin"), $$P(C \; | \; F)$$ which says that the probability of the class $C$ is conditioned on the visibility of the feature $F$. Using Bayes' theorem we can write $$P(C \; | \; ...


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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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This would be more suitable as a comment but I don't have enough points; but here's my opinion. Optimisation algorithms like gradient descent are iterative algorithms. So it is rarely possible that they arrive at the minima in 1 epoch. A single epoch means that all data points have been visited once or a certain number of data samples have been taken from a ...


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Let's quickly get out our copies of Deep Learning by Goodfellow et al. (2016). More specifically, I'm referring to page 276. On this page, the authors argue for a relatively small minibatch size, since there are less than linear returns for estimating the gradient when increasing the minibatch size. Returns here refer to the reduction of the standard error ...


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I read the article you linked, and what you are missing are that the given conversion probabilities are assessed pre-callback - i.e. they include an assessment of whether you will even call them back or not. So of course the probabilities change if you change your behaviour. The writer of the article has created a bit of a straw man argument by defining a ...


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You should not use augmented data in the validation nor in the test set. Validation and test set are purely used for hyperparameter tuning and estimating the final performance, i.e. estimating the generalization error. These two data sets should be as close as possible to other data, which you could have acquired, but you actually haven not, i.e. your true ...


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Since you have a multiclass classification problem rather than a binary classification problem (i.e. a two-class problem), I recommend to adjust your architecture and use softmax instead of sigmoid as final activation function and categorcal_crossentropy instead of binary_crossentropy. Softmax will ensure all your outputs are valid probabilities. This is ...


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The original paper by Sergey Ioffe and Christian Szegedy; https://arxiv.org/abs/1502.03167 "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" is very good. Make sure to go through the paper slowly and make annotations to truly understand it.


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