Differences between MVC and MVT framework

MVC (Model-View-Controller) is a software design pattern for developing web applications. It consists of the following parts:

Model: It is responsible for managing data of the application. It can be a database, a single object, or some structure of objects etc. It can also have logic to update controller if its data changes.

View: View represents the visualization of the data that a model contains. It is responsible for which data should be displayed for a particular request.

Controller: It connects the View with the Model. It controls the data flow and keeps Model and View separate. It is responsible for responding to the user input and perform interactions on the data model objects.

Django - MVT (Model-View-Template) pattern is slightly different from MVC pattern. The main difference is Django itself take care of the Controller part. In the Template, it's just HTML file mixed with DTL - Django Template Language.

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DFS Performance Measurement

Completeness DFS is not complete, to convince yourself consider that our search start expanding the left subtree of the root for so long path (maybe infinite) when different choice near the root could lead to a solution, now suppose that the left subtree of the root has no solution, and it is unbounded, then the search will continue going deep infinitely, in this case , we say that DFS is not complete. Optimality Consider the scenario that there is more than one goal node, and our search decided to first expand the left subtree of the root where there is a solution at a very deep level of this left subtree , in the same time the right subtree of the root has a solution near the root, here comes the non-optimality of DFS that it is not guaranteed that the first goal to find is the optimal one, so we conclude that DFS is not optimal. Time Complexity Consider a state space that is identical to that of BFS, with branching factor b, and we start the search fro...

Regularization in Deep Learning / Machine Learning - Prevent Overfitting

image source: mlexplained Overfittng happens in every machine learning (ML) problem. Learning how to deal with overfitting is essential to mastering machine learning. The fundamental issue in machine learning is the tension between optimization and generalization. Optimization refers to the process of adjusting a model to get the best performance possible on the training data (the learning in machine learning ), whereas generalization refers to how well the trained model performs on data it has never seen before . The goal of the game is to get good generalization, of course, but you don’t control generalization; you can only adjust the model based on its training data. The processing of fighting overfitting is a way called regularization . [1]. How do you know whether a model is overfitting? The best initial method is to measure error on a training and test set. If you see a low error on the training set and...

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