Ankit Das

Ankit Das

A data analyst with expertise in statistical analysis, data visualization ready to serve the industry using various analytical platforms. I look forward to having in-depth knowledge of machine learning and data science. Outside work, you can find me as a fun-loving person with hobbies such as sports and music.
human_activities
Developers Corner

Have you Heard About the Video Dataset of Day to day Human Activities

ActivityNet is an enormous dataset that covers exercises that are generally pertinent to how people invest their energy in their everyday living. It was developed in 2015 by the researchers: Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanemand Juan Carlos Niebles1. ActivityNet gives tests from 203 movement classes with a normal of 137 untrimmed recordings per class and 1.41 movement occurrences per video, for an aggregate of 849 video hours.

video_dataset
Developers Corner

How To Use UCF101, The Largest Dataset Of Human Actions

UCF-101 dataset has 101 actions and 13320 clips of human actions, collected from youtube were first introduced in 2012 by researchers: Khurram Soomro, Amir Roshan Zamir and Mubarak Shah of Center for Research in Computer Vision, Orlando, FL 32816, USA. The clips in the action class are divided into 25 groups. Each group contains 4-7 clips. Clips in each group share some common features like background or actor.

loss_function
Developers Corner

Loss Functions in Deep Learning: An Overview

Neural Network uses optimising strategies like stochastic gradient descent to minimize the error in the algorithm. The way we actually compute this error is by using a Loss Function. It is used to quantify how good or bad the model is performing. These are divided into two categories i.e.Regression loss and Classification Loss.

LSTM Autoencoder
Developers Corner

Introduction to LSTM Autoencoder Using Keras

LSTM autoencoder is an encoder that makes use of LSTM encoder-decoder architecture to compress data using an encoder and decode it to retain original structure using a decoder.

Gaussian mixture
Developers Corner

Gaussian Mixture Model Clustering Vs K-Means: Which One To Choose

In recent times, there has been a lot of emphasis on Unsupervised learning. Studies like customer segmentation, pattern recognition has been a widespread example of this which in simple terms we can refer to as Clustering. We used to solve our problem using a basic algorithm like K-means or Hierarchical Clustering. With the introduction of Gaussian mixture modelling clustering data points have become simpler as they can handle even oblong clusters. It works in the same principle as K-means but has some of the advantages over it.

Language_model
Developers Corner

Complete Guide on Language Modelling: Unigram Using Python

Language modelling is the speciality of deciding the likelihood of a succession of words. These are useful in many different Natural Language Processing applications like Machine translator, Speech recognition, Optical character recognition and many more.In recent times language models depend on neural networks, they anticipate precisely a word in a sentence dependent on encompassing words. However, in this project, we will discuss the most classic of language models: the n-gram models.

Knowledge Graph
Developers Corner

Complete Guide to Implement Knowledge Graph Using Python

Information Extraction is a process of extracting information in a more structured way i.e., the information which is machine-understandable. It consists of subfields which cannot be easily solved. Therefore, an approach to store data in a structured manner is Knowledge Graph which is a set of three-item sets called Triple where the set combines a subject, a predicate and an object.

Principal Component
Developers Corner

Principal Component Analysis On Matrix Using Python

Machine learning algorithms may take a lot of time working with large datasets. To overcome this a new dimensional reduction technique was introduced. If the input dimension is high Principal Component Algorithm can be used to speed up our machines.

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