You may observe, if x=1,y will roughly be equal to 6 and if x=2,y will be around 8.5. If you plot the data, you can see a positive relationship between your independent variable, x and your dependent variable y. Imagine you have two variables, x and y and your task is to predict the value of knowing the value of. How to train a Linear Regression with TensorFlowīefore we begin to train the model, let’s have a look at what is a linear regression.In this TensorFlow Regression tutorial, you will learn: In a second part, you will use the Boston dataset to predict the price of a house using TensorFlow estimator. The first part of the tutorial explains how to use the gradient descent optimizer to train a Linear regression in TensorFlow. The computations are faster and are easier to implement. In this tutorial, you will use the estimators only. TensorFlow provides a toolbox called estimator to construct, train, evaluate and make a prediction. Low-level API: Build the architecture, optimization of the model from scratch.On top of that, TensorFlow is equipped with a vast array of APIs to perform many machine learning algorithms. TensorFlow provides tools to have full control of the computations. The relationship with one explanatory variable is called simple linear regression and for more than one explanatory variables, it is called multiple linear regression. This modelling is done between a scalar response and one or more explanatory variables. Linear Regression is an approach in statistics for modelling relationships between two variables.
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