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I think U are looking for PLSA for PLSA either U find out those topics(catogeries) with EM or NNMF Personally I recommend NNMF or u can use LDA which is Bayesian version of PLSA here is code for PLSA: https://github.com/Man-ash/Probabilistic-Latent-semantic-analysis which use NNMF for EM method I code it by myself but i am not sure if it is right https://...


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Smoothness here is the mathematical definition, so as you implied smoothness is ruled out by output data with sharp spikes or discontinuous jumps (and possibly the data of the gradient, the gradient's gradient, ad infinitum, depending on who defines smoothness). By any definition a lot of activation functions are not smooth, for example RELU. This means ...


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One way you can definitely approach the problem is by using (Deep) Reinforcement Learning (DRL). YouTube is actually using DRL as well to suggest videos to users in order to maximize users' engagement with their website. For more information (and further references to papers explaining how other major companies implement their recommendation systems), see ...


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I do not feel comfortable proclaiming that I would be a machine learning expert. But I want to point out that there is indeed interest in applying tensor networks in machine learning settings. Let me highlight three particular settings in the following. They can be used to find the governing equations behind dynamical systems analogous to the SINDy algorithm....


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I found: For scikit-learn like models: MicroML, Micro-LM, Micro Learn, sklearn-porter, emlearn For deep learning models: tensorflow Lite Micro, X-CUBE-AI, Glow, NNoM These seems to partly fit my needs. But i am surprised that i cannot find something more general that either convert Python to C or to object file with ML support (to be used in C projects). ...


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If the library running the model can be compiled for your microcontroller, then you can run your model on that microcontroller. If you train using one library and deploy using another library, you possible can convert your model to that library: ONNX. Some library links on Edge Computing in ML: Microcontroller support for Tensorflow Lite Edge ML PyTorch ...


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There are a few possible approaches to deploying a ML model to a microcontroller. The main limiting factor to deployment on microcontollers is that ML models are usually a representation of a set of parameters that are intended to be used as input to a prediction algorithm alongside a new datapoint. Most such models assume the presence of an accompanying ...


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If you do not specify an activation for a layer you are effectively creating a linear transformation through that layer. From the documentation: activation: Activation function to use. If you don't specify anything, no activation is applied (see keras.activations).


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My understanding is that AI can be understood as a very generalized and abstract statistics software package handling input data in a general way to find the "best fit" to some form of problem. Is that correct? I know it isn't. But is it vaguely correct? No. It's not correct, in my opinion, not even vaguely and in many ways. AI is not (...


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No, the original (or any) YOLO is for object detection. You can easily replace the feature extractor (DarkNet53, if I'm not mistaken) with any other, as long as you maintain the correct number of weights in the detection layer.


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Adding to nbro's solution, the less you have to normalize/balance/preprocess/augment, the better, e.g. because then you know for sure that the accuracy is the achievement of the model rather than data combing. For example, if you can achieve the same accuracy using two approaches (e.g. with the image dataset): for each image, subtract global mean, divide by ...


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Yes, sure, data pre-processing is also done in deep learning. For example, we often normalize (or scale) the inputs to neural networks. If the inputs are images, we often resize them so that they all have the same dimensions. Of course, the pre-processing step that you apply depends on your data, neural network, and task. Here or here are two examples of ...


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This is actually a highly technical term which has been kind of misused and overgenralized in many places. What does 'convergence' mean in a literal sense? It simply means that a series of terms indexed by $\mathbb{N}$ ($X_1,X_2,X_3,..$) tends to a certain fixed value say $X$ as $\mathbb{N} \rightarrow \infty$, but may not achieve the fixed value. (there are ...


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Not sure about which function to call in GPFlow, but the principle is simple. If your $Y$ data was scaled: $$Y_{scaled} = {(Y - mean(Y)) \over {sd(Y)}}$$ So now if you have any prediction (from the scaled model) $\hat{Y}_{scaled}$, with stand error $\sigma_{\hat {Y}_{scaled}}$, then it is a normal distribution ${\cal N}(\hat {Y}_{scaled}, \sigma^2_{\hat {Y}_{...


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Is there a term for the humans who do [machine] learning? Typically you will see "AI researchers" for people studying machine intelligence in general, or "data scientists" for people working with statistics or studying specific solutions in machine learning. Both those terms are used quite flexibly, and generally understood to be ...


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Since the data is linearly separable linear model $y = w^Tx$ will be able to perfectly classify all the examples. That means that loss functions $L_1(w), L_3(w)$ and $L_4(w)$ will have a value of 0 (since all examples are correctly classified). For the loss $L_2(w)$ second term will be 0 if all examples are correctly classified. The first term of $L_2(w)$ \...


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First things first. What is an optimal $w$?. In this case it is supposed to be not only the one that minimizes the emprical /sample loss, but also non-trivial as we shall sonn see. Now inspect the loss functions, we see a term $-y_iw^Tx_i$ coming up. What exactly is this term? It can be anything. The correct term would have been $-y_i(w^Tx_i + b)$, or ...


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A critical goal of training a neural network is to minimize the loss. Loss is not explained for spaCy because it is a general concept for machine learning and deep learning. Loss is not specific to spaCy and although there are some finer details I don't believe that is your inquiry. In general, to understand loss functions, I recommend the following ...


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The probability density is used to 'measure how good' the parameters are because it is a natural way of quantifying if these parameters are good for the observed data. Also, as the notation often causes some confusion, $L(\theta | x)$ denotes the probability of all of your observed data, not just one value. Also the "$|$" may cause confusion as it ...


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In the least-squares SVM (LS-SVM) the non-zero Lagrange multipliers ($\alpha$) are the support values. The corresponding data points are the support vectors. Johan Suykens explains this in Least Squares Support Vector Machines.


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It sounds like you may be looking for the A* search algorithm. It is a search algorithm, like DFS and BFS, but it will explore only the most promising branches based on a heuristic function you supply. The difficult part of implementing this is deciding on a low-cost, admissible heuristic. Excited to reflect nbro's suggestion from comments.


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There's nothing stopping you from training a model with whatever tags you want. Using what you describe as "usual" format means you would have approx half as many tags as using the IOB format. In theory this means your model will develop higher accuracy faster and with less training data. On the downside, you will need to do more work when ...


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Generally, if one googles "quantum machine learning" or anything similar the general gist of the results is that quantum computing will greatly speed up the learning process of our "classical" machine learning algorithms. This is correct. A lot of machine learning methods involve linear algebra, and it often takes far fewer quantum ...


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The difference is much simpler than you might have anticipated: In the quantum computing community, machine learning algorithms designed to be used on quantum computers as opposed to classical computers, would fall under "quantum machine learning". There's really nothing more to it! There is a short paper published in Nature called "Quantum ...


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Every model is a function. Not every function is a model. A function uniquely maps elements of some set to elements of another set, possibly the same set. Every AI model is a function because they are implemented as computer programs and every computer program is a function uniquely mapping the combination of the sequence of bits in memory and storage at ...


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Any model can be considered to be a function. The term "model" simply denotes a function being used in a particular way, namely to approximate some other function of interest.


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Embedding is the process of representing data (from a source domain) in a new (or target) domain. Usually, the source domain is discrete, and the target domain is continuous. For example, embedding words into the continuous vector space can be done by the word2vec method. The main reason behind using the embedding is doing meaningful mathematical ...


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In simple terms, a neural network model is a function approximator which tries to fit the curve of the hypothesis function. A function itself has an equation which will generate a fixed curve: If we have the equation (i.e., the function), we do not need neural network for its input data. However, when we only have some notion of its curve (or the input and ...


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Although this may not be applicable to all cases, I like to think of a model as a set of functions, so here's the difference. Why is this definition useful? If you think of a neural network with a vector of parameters $\theta \in \mathbb{R}^m$ as a model, then a specific combination of these parameters represents a specific function. For example, suppose ...


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Softmax activation always adds up to 1, because it's designed to deal with probabilities (in problems of classification, those probabilities represent how likely the network thinks an object belongs to a specific class). You can verify that by summing up the numbers of your output layer. So currently your network is trying to do the impossible, to produce ...


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One kind of system you could look into are Echo State Networks (ESNs). They are relatively cheap to train and can learn to predict output signals to an arbitrary degree of precision. All you need to train the system is some labeled training data. Thus, if you have a sequence of measurements/feature values and the corresponding sequence of class labels, you ...


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Of course you can use AI (specially Deep Learning) in your application. your covariates will be the input to your AI model and the model should predict probability of presence. The model has no problem with binary data and binary data is common in this field. Also note that 1:100 ratio is not good and the network will probably learn to output absence for any ...


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