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Jinja is a web template engine for the Python programming language.It was created by Armin Ronacher and is licensed under a BSD License.Jinja is similar to the Django template engine, but provides Python-like expressions while ensuring that the templates are evaluated in a sandbox.
Django (/ ˈ dʒ æ ŋ ɡ oʊ / JANG-goh; sometimes stylized as django) [6] is a free and open-source, Python-based web framework that runs on a web server. It follows the model–template–views (MTV) architectural pattern .
Function keys on keyboards are a form of soft key. In contrast, a hard key is a key with dedicated function such as the keys on a number keypad. Screen-labeled function keys are today most commonly found in kiosk applications, such as automated teller machines and gas pumps. Screen-label function keys date to aviation applications in the late ...
An example of unsupervised dictionary learning is sparse coding, which aims to learn basis functions (dictionary elements) for data representation from unlabeled input data. Sparse coding can be applied to learn overcomplete dictionaries, where the number of dictionary elements is larger than the dimension of the input data. [ 21 ]
An example is the anonymous function which squares its input, called with the argument of 5: f = lambda x : x ** 2 f ( 5 ) Lambdas are limited to containing an expression rather than statements , although control flow can still be implemented less elegantly within lambda by using short-circuiting, [ 20 ] and more idiomatically with conditional ...
But if the true function is highly complex (e.g., because it involves complex interactions among many different input features and behaves differently in different parts of the input space), then the function will only be able to learn with a large amount of training data paired with a "flexible" learning algorithm with low bias and high variance.
Example of hidden layers in a MLP. In artificial neural networks, a hidden layer is a layer of artificial neurons that is neither an input layer nor an output layer. The simplest examples appear in multilayer perceptrons (MLP), as illustrated in the diagram. [1] An MLP without any hidden layer is essentially just a linear model.
Figure 1. Probabilistic parameters of a hidden Markov model (example) X — states y — possible observations a — state transition probabilities b — output probabilities. In its discrete form, a hidden Markov process can be visualized as a generalization of the urn problem with replacement (where each item from the urn is returned to the original urn before the next step). [7]