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Linear regression is a statistical method for estimating the relationship between two variables. With an understanding of linear regression, you can make predictions about how changing one variable will affect another.

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Great introduction to Spark with Python. Must - have material to learn basic Spark data analysis

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Here you can find a simple explanation to Attention Mechanism in Deep Learning.

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I will try to explain how I can use LSTMs and GRUs in order to implement a very basic feed forward network that can classify characters in a training set.

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A blog about machine learning and other interesting deep learning projects

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This is a tutorial for Pytorch beginners on how to build a feedforward neural network. The tutorial starts with a linear regression model and ends at a 2-layer feedforward neural network.

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Gradient descent is the most popular optimization algorithm used in Deep Learning. It's used to calculate parameters for many different kinds of architectures, but there are also many different variants of it. The purpose of this post is not to explain what gradient descent is

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Gradient descent (GD) is an optimization algorithm that was derived by solving the quadratic programming (QP). A set of input values, an initial value for the output value, and its cost are required to implement GD, which is specified

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In this article, we will explore a number of issues around noise in machine learning and deep learning. We will see what causes the noise and how to get rid of it.

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A proof of concept creation of a missing value imputing system using Scikit-Learn models.