Linear Regression in 5 mins using Tensorflow 2x | Tamil
Linear Regression in 5 mins using Tensorflow 2x | Tamil
Linear regression is a widely used statistical method for modeling the relationship between a dependent variable and one or more independent variables. TensorFlow is a popular open-source software library for data processing, machine learning, and deep learning applications. Here are some advantages and disadvantages of using Tensorflow for linear regression:
In the case of Linear regression, the hypothesis is a straight line, i.e, h(x) = wx + b Where w is a vector called Weights and b is a scalar called Bias. The Weights and Bias are called the parameters of the model. Linear Regression is a very common statistical method that allows us to learn a function or relationship from a given set of continuous data. For example, we are given some data points of x and corresponding y and we need to learn the relationship between them which is called a hypothesis.
In this tutorial, we will predict the fuel efficiency between mileage and Litters of petrol.
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