INTRODUCTION: When we have deep neural networks, the biggest problem is overfitting. We can say a neural network overfits when it has excellent performance in the training data and has a poor performance in unseen data. […]

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## Introduction to activation functions

Why do we need activation functions? We use activation function to introduce non-linearity into the neural network. Activation functions convert independent variables of near infinite range into simple probabilities between 0 and 1. Now the question […]

Continue readingMore Tag## Introduction to Principal Component Analysis(PCA)

Principal Component Analysis is an unsupervised dimensionality reduction technique, which is extensively used in machine learning. It helps us to alleviate the problem of the curse of dimensionality by reducing the dimension of the data. PCA […]

Continue readingMore Tag## Introduction to T-SNE with implementation in python

INTRODUCTION to T – SNE: T-SNE is a non-linear dimensionality reduction technique used to visualize high-dimensional data in two or more dimensions. Unlike PCA which preserves only the global structure of the data T-SNE preserves both the local […]

Continue readingMore Tag## Distance/Similarity Measures in Machine Learning

INTRODUCTION: For algorithms like the k-nearest neighbor and k-means, it is essential to measure the distance between the data points. In KNN we calculate the distance between points to find the nearest neighbor, and in K-Means […]

Continue readingMore Tag## Introduction to Machine Learning

What is machine learning? So what is machine learning anyway? ML is a subdomain or a subfield of artificial intelligence. In the last few years, machine learning has become one of the most important branches of […]

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