In this post, we will be looking into any() and all() functions in Python. First, let us discuss the any() function. 👉 any() The any() function takes an iterable as an argument: any(iterable). The iterable can be a list, tuple or dictionary. The any() function returns ‘True’ if any element in the…

As you may be knowing Logistic Regression is a Machine Learning algorithm. It is used for binary classification problems. We also have multiclass logistic regression, where we basically re-run the binary classification multiple times. It is a linear algorithm with a non-linear transform at the output. The input values are…

We have seen the basic operation of convolution in the previous post. Knowing Convolution Basics In this post, we will be discussing padding in Convolutional Neural Networks. Padding is the number of pixels that are added to an input image. …

In this article, we are going to learn about the grayscale image, colour image and the process of convolution. Grayscale image A grayscale image where the image is represented as only the shades of grey. The intensity of the various pixels of the image is denoted using the values from 0 to…

The gradient descent algorithm is an approach to find the minimum point or optimal solution for a given dataset. It follows the steepest descent approach. That is it moves in the negative gradient direction to find the local or global minima, starting out from a random point. …

Today we are going to discuss some of the metrics in classification. Let us first look at the confusion matrix. The Accuracy is calculated as:-

Today let’s look at the topic population and sample. Population refers to the collection of all the elements in a dataset. It is usually a large number and hence the processing of data of the whole population is a tedious task. Population data is hard to observe and to deal…

It is a type of continuous probability distribution for a random variable. It is also called as Normal distribution. The general form of its probability density function is; Here the parameter µ is the mean or expectation of the distribution and σ is the standard deviation. When the mean becomes…

A probability distribution is a function that describes the chances of finding a random variable over a defined range. Below is the list of some of the most used probability distribution functions. Normal(Gaussian) distribution. Log-Normal distribution. Bernoulli distribution. Binomial distribution. Poisson distribution. Today let’s see what are Bernoulli, Binomial and…