From 32df66afceaeb8f558096c5676fd626cfe7358fd Mon Sep 17 00:00:00 2001 From: Navan Chauhan Date: Wed, 29 Jan 2020 11:14:30 +0530 Subject: Publish deploy 2020-01-29 11:14 --- posts/2019-12-16-TensorFlow-Polynomial-Regression/index.html | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) (limited to 'posts/2019-12-16-TensorFlow-Polynomial-Regression/index.html') diff --git a/posts/2019-12-16-TensorFlow-Polynomial-Regression/index.html b/posts/2019-12-16-TensorFlow-Polynomial-Regression/index.html index 0e3dd19..ad90be2 100644 --- a/posts/2019-12-16-TensorFlow-Polynomial-Regression/index.html +++ b/posts/2019-12-16-TensorFlow-Polynomial-Regression/index.html @@ -1,4 +1,4 @@ -Polynomial Regression Using TensorFlow | Navan Chauhan
Navan Chauhan
🕑 16 minute read.

Polynomial Regression Using TensorFlow

In this tutorial you will learn about polynomial regression and how you can implement it in Tensorflow.

In this, we will be performing polynomial regression using 5 types of equations -

  • Linear
  • Quadratic
  • Cubic
  • Quartic
  • Quintic

Regression

What is Regression?

Regression is a statistical measurement that is used to try to determine the relationship between a dependent variable (often denoted by Y), and series of varying variables (called independent variables, often denoted by X ).

What is Polynomial Regression

This is a form of Regression Analysis where the relationship between Y and X is denoted as the nth degree/power of X. Polynomial regression even fits a non-linear relationship (e.g when the points don't form a straight line).

Imports

import tensorflow.compat.v1 as tf
+Polynomial Regression Using TensorFlow | Navan Chauhan
Navan Chauhan
🕑 16 minute read.
Polynomial Regression Using TensorFlow
In this tutorial you will learn about polynomial regression and how you can implement it in Tensorflow.
In this, we will be performing polynomial regression using 5 types of equations -
  • Linear
  • Quadratic
  • Cubic
  • Quartic
  • Quintic
Regression
What is Regression?
Regression is a statistical measurement that is used to try to determine the relationship between a dependent variable (often denoted by Y), and series of varying variables (called independent variables, often denoted by X ).
What is Polynomial Regression
This is a form of Regression Analysis where the relationship between Y and X is denoted as the nth degree/power of X. Polynomial regression even fits a non-linear relationship (e.g when the points don't form a straight line).
Imports
import tensorflow.compat.v1 as tf
 tf.disable_v2_behavior()
 import matplotlib.pyplot as plt
 import numpy as np
@@ -366,4 +366,4 @@
 plt.show()
 

 
-
Results and Conclusion
You just learnt Polynomial Regression using TensorFlow!
Notes
Overfitting
> Overfitting refers to a model that models the training data too well.Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. The problem is that these concepts do not apply to new data and negatively impact the models ability to generalize.
Source: Machine Learning Mastery
Basically if you train your machine learning model on a small dataset for a really large number of epochs, the model will learn all the deformities/noise in the data and will actually think that it is a normal part. Therefore when it will see some new data, it will discard that new data as noise and will impact the accuracy of the model in a negative manner
Tagged with: 

\ No newline at end of file
+

Results and Conclusion

You just learnt Polynomial Regression using TensorFlow!

Notes

Overfitting

> Overfitting refers to a model that models the training data too well.Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. The problem is that these concepts do not apply to new data and negatively impact the models ability to generalize.

Source: Machine Learning Mastery

Basically if you train your machine learning model on a small dataset for a really large number of epochs, the model will learn all the deformities/noise in the data and will actually think that it is a normal part. Therefore when it will see some new data, it will discard that new data as noise and will impact the accuracy of the model in a negative manner

Tagged with:
\ No newline at end of file -- cgit v1.2.3