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Also, unlike the Django template engine, Jinja allows the template designer to call functions with arguments on objects. Jinja is Flask's default template engine [6] and it is also used by Ansible, [7] Trac, and Salt. [8] It is also used to make SQL macros, for example for use with dbt. [9]
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 .
Input layer: One neuron appears in the input layer for each predictor variable. In the case of categorical variables, N-1 neurons are used where N is the number of categories. The input neurons standardizes the value ranges by subtracting the median and dividing by the interquartile range. The input neurons then feed the values to each of the ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
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.
The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights. Nontrivial problems can be solved using only a few nodes if the activation function is nonlinear .
A perceptron traditionally used a Heaviside step function as its nonlinear activation function. However, the backpropagation algorithm requires that modern MLPs use continuous activation functions such as sigmoid or ReLU. [8] Multilayer perceptrons form the basis of deep learning, [9] and are applicable across a vast set of diverse domains. [10]
Function prototypes include the function signature, the name of the function, return type and access specifier. In this case the name of the function is "Sum". The function signature defines the number of parameters and their types. The return type is "void". This means that the function is not going to return any value.