One of the most common problems of training a deep neural network is that it overfits. Overfitting occurs when the network learns specific patterns in the training data and is unable to generalize well over new […]

Continue readingMore Tag# Author: Niranjan B Subramanian

## Simple Linear Regression using Keras

Regression is a statistical approach used for predicting real values like age, weight, salary, for example. In regression, we have a dependent variable which we want to predict using some independent variables. The goal of regression […]

Continue readingMore Tag## KNN from Scratch

The K-Nearest Neighbor(KNN) classifier is one of the easiest classification methods to understand and is one of the most basic classification models available. KNN is a non-parametric method which classifies based on the distance to the […]

Continue readingMore Tag## Singular Value Decomposition for Dimensionality Reduction

Singular Value Decomposition usually referred to as SVD, is a type of matrix decomposition technique that is popularly used to reduce the dimensions of the data. SVD decomposes a mxn real matrix A into a product […]

Continue readingMore Tag## WordClouds in Python

In this article, we are going to see how to create a word cloud in python. A word cloud creates a collage of most prominent words from the given text. The size of each word is […]

Continue readingMore Tag## KERAS Callbacks

Keras comes with a long list of predefined callbacks that are ready to use. Keras callbacks are functions that are executed during the training process. According to Keras Documentation, A callback is a set of functions to be […]

Continue readingMore Tag## Types of Data

It is necessary to understand the different types of data we are dealing with to choose the right visualization technique or statistical measure for our data. In this blog post, we’ll discuss two types of data […]

Continue readingMore Tag## Introduction to Hyper-parameter Tuning: GridSearchCV and RandomSearchCV

Most of the machine learning algorithms contain a number of hyperparameters that we can tune to improve the model’s performance. The hyperparameter controls the behaviors of the algorithm and has an enormous impact on the results. […]

Continue readingMore Tag## Measure of Central Tendency and Measure of Spread

Summarizing the quantitative data can help us understand them better. In this article, we’ll see various methods to summarize quantitative data by the measure of central tendency(such as mean, median, and mode) and by the measure […]

Continue readingMore Tag## Why High Dimensional Data are a Curse?

In the era of big data, massive datasets(not only in the number of samples being collected but also in the number of features) are increasingly prevalent in many disciplines and are often difficult to interpret. In […]

Continue readingMore Tag