Visualizing Cross-validation Code

Visualizing Cross-validation Code  #MachineLearning #dataviz

  • Let us say, you are writing a nice and clean Machine Learning code (e.g. Linear Regression).
  • As the name of the suggests, cross-validation is the next fun thing after learning Linear Regression because it helps to improve your prediction using the K-Fold strategy.
  • But we divide the dataset into equal K parts (K-Folds or cv).
  • Then train the model on the bigger dataset and test on the smaller dataset.
  • This graph represents the k- folds Cross Validation for the Boston dataset with Linear Regression model.


Cross-validation helps to improve your prediction using the K-Fold strategy. What is K-Fold you asked? Check out this post for a visualized explanation.

Continue reading “Visualizing Cross-validation Code”

The Gentlest Introduction to Tensorflow – Part 2

The Gentlest Introduction to Tensorflow – Part 2  #NeuralNetworks

  • The goal in linear regression is to find W, b, such that given any feature value (x), we can find the prediction (y) by substituting W, x, b values into the model.
  • However to find W, b that can give accurate predictions, we need to ‘train’ the model using available data (the multiple pairs of actual feature (x), and actual outcome (y_), note the underscore).
  • We also need to define a cost function, which is a measure of the differencebetween the prediction (y) for given a feature value (x), and the actual outcome (y_) for that same feature value (x).
  • Our linear model and cost function equations [A] can be represented as TF graph as shown:

    Next, we select a datapoint (x, y_) [C], and feed [D] it into the TF Graph to get the prediction (y) as well as the cost.

  • Using a variety of datapoints generalizes our model, i.e., it learns W, b values that can be used to predict any feature value.

 
In the previous article, we used Tensorflow (TF) to build and learn a linear regression model with a single feature so that given a feature value (house size/sqm), we can predict the outcome (house price/$).
Continue reading “The Gentlest Introduction to Tensorflow – Part 2”

The Gentlest Introduction to Tensorflow – Part 2

The Gentlest Introduction to #Tensorflow Part 2  #DeepLearning #MachineLearning @reculture_us

  • Calculate prediction (y) & cost using a single datapoint
  • Using a variety of datapoints generalizes our model, i.e., it learns W, b values that can be used to predict any feature value.
  • For simplicity, we use least minimum squared error (MSE) as our cost function.
  • Create a TF Graph with model & cost, and initialize W, b with some values
  • We select a datapoint (x, y [C], and feed [D] it into the TF Graph to get the prediction (y) as well as the cost.

Read the full article, click here.


@kdnuggets: “The Gentlest Introduction to #Tensorflow Part 2 #DeepLearning #MachineLearning @reculture_us”


 
In the previous article, we used Tensorflow (TF) to build and learn a linear regression model with a single feature so that given a feature value (house size/sqm), we can predict the outcome (house price/$).


The Gentlest Introduction to Tensorflow – Part 2

Deep Learning Prerequisites: Linear Regression in Python Udemy Coupon 40%

Deep Learning Prerequisites: Linear Regression in Python Hy_e7To4
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#python #programming

  • The course teaches you about one popular technique used in machine learning, data science and statistics: linear regression.
  • If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you.
  • Everything needed (Python, and some Python libraries) can be obtained for free.
  • Viraldeal.net is to help keep some money in your wallets and does so by providing thousands of coupon codes and shopping deals to popular online stores.
  • The course does not require any external materials.

Read the full article, click here.


@python_devv: “Deep Learning Prerequisites: Linear Regression in Python Hy_e7To4

#python #programming”


This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python.


Deep Learning Prerequisites: Linear Regression in Python Udemy Coupon 40%