Then any pair of words will be removd from each other by the same distance. But a model with so many dimensions of the feature space may simply not fit into any realistic amount of memory. In addition if we want our classifier to train well and work stably it is very desirable that words that are close in meaning have close representations and vice versa. For this embedding is use – a model that being trains on a large amount of text is able to transform words into vectors in a multidimensional usually several hundreds dimensions space in such a way that words that are close in meaning appear next to each other. The most famous text embedding is probably Word Vic.
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It is designd in such a way that words that are often mentiond in the same context get close representations and moreover the mutual arrangement of vectors can reflect semantic relationships between words Belarus Email List for example. Another example of embedding is face identification. If we are facd with the task of distinguishing between a limitd number of previously known people we can train a classifier. But in fact another case is much more likely when the problem is posd as follows: There are two photographs. The model must determine whether they depict the same person or two different people.
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For this purpose embedding is traind in such a way that the distance between the vectors of two images of one person is less and the distance between the vectors of images of two different people is greater than some fixd threshold value. Artificial neural networks Above I promisd to talk about neural networks. I am keeping my promise. At the same time BTC Email List let’s see if they can be applid to our problem about personal offers. Artificial neural networks are a very popular and very versatile type of machine learning models. The authors of the concept of neural networks trid to reflect in it the principles of the living brain. A simple multilayer fully connectd ANN the so-calld perceptron is arrangd as follows Fig. fig _neural. Artificial neural network Perceptron consists of several layers of so-calld neurons.