As a career Data-Scientist, all through your life you have to deal with Matrix form of data where data in Numpy or Pandas or TensorFlow where Axis and Dimensions are the fundamental structural concept.
Basic Attributes of the ndarray Class
Let's consider the below array
The “shape” of this array is a tuple with the number of elements per axis (dimension). In our example, the shape is equal to (6, 3), i.e. we have 6 lines and 3 columns.
Numpy has a function called “shape” which returns the shape of an array. …
The target of this blog post is to discuss the concept around and the Mathematics behind the below formulation of Bias-Variance Tradeoff.
And in super simple term
Total Prediction Error = Bias + Variance
The goal of any supervised machine learning model is to best estimate the mapping function (f) for the output/dependent variable (Y) given the input/independent variable (X). The mapping function is often called the target function because it is the function that a given supervised machine learning algorithm aims to approximate.
The Expected Prediction Error for any machine learning algorithm can be broken down into three parts:
In this article, I shall go over the topic of arriving at the Vectorized Gradient-Descent formulae for the Cost function of the for Matrix form of training-data Equations. And along with that the Fundamentals of Calculus (especially Partial Derivative) and Matrix Derivatives necessary to understand the process.
So our target of this article is to understand the full Mathematics and the flow behind arriving at the below formulae, which is the Vectorized Gradient of the training-data Matrix
A matrix A over a field K or, simply, a matrix A (when K is implicit) is a rectangular array of scalars usually presented in the following…