Human Activity Recognition using high-dimensional visual streams has been gaining popularity in recent times. Using a video input to categorize human activity is primarily applied in surveillance of different kinds. At hospitals and nursing homes, this can be used to immediately alert caretakers when any of the residents are displaying any sign of sickness — clutching their chest, falling, vomiting, etc. At public places like airports, railway stations, bus stations, malls or even in your neighborhoods, the activity recognition becomes the means to alert authorities on recognizing suspicious behavior. …
High Accuracy Satellite Image Segmentation
Satellite image segmentation has been in practice for the past few years, it has a wide range of real-world applications like monitoring deforestation, urbanization, traffic, identification of natural resources, urban planning, etc.
We all know image segmentation is color coding each pixel of the image into either one of the training classes. Satellite image segmentation is same as image segmentation, in this we use landscape images taken from satellites and perform segmentation on them. Typical training classes include vegetation, land, buildings, roads, cars, water bodies, etc.
Many Convolution Neural Network (CNN) models have shown decent…
Suppose you are looking for a product on a particular website. As soon as you commence on the journey of making; the first search for a product, fidgeting on the idea to either buy it or not, and finally purchasing it, you are targeted or tempted by various marketing strategies through; various channels to buy the product.
You may start seeing the ads for the particular product on social media websites, on the side of various web pages, receive promotional emails, etc. This entire experience through these different channels that you interact with; will be referred to as touchpoints.
Natural Language Inferencing (NLI) task is one of the most important subsets of Natural Language Processing (NLP) which has seen a series of development in recent years. There are standard benchmark publicly available datasets like Stanford Natural Language Inference (SNLI) Corpus, Multi-Genre NLI (MultiNLI) Corpus, etc. which are dedicated to NLI tasks. Few state-of-the-art models trained on these datasets possess decent accuracy.
In this blog I will start with briefing the reader about NLI terminologies, applications of NLI, NLI state-of-the-art model architectures and eventually demonstrate the NLI task using Kaggle Contradictory My Dear Watson Challenge Dataset by the end.
1. What is Store Traffic Analytics?
In-store traffic analytics allows data-driven retailers to collect meaningful insights about customer’s behavioral data.
The retail industry receives millions of visitors every year. Along with fulfilling the primary objective of a store, it is can also extract valuable insights from this constant stream of traffic.
The footfall data, or the count of people in a store, creates an alternate source of value for retailers. One can collect traffic data and analyze key metrics to understand what drives the sales of their product, customer behavior, preferences, and related information.
2. How does it help store…
“Cinema is a mirror by which we often see ourselves.” — Alejandro Gonzalez Inarritu
If 500 people saw a movie, there exist 500 different versions of the same idea conveyed by the movie. Often movies reflect culture, either what the society is or what it aspires to be.
A hotel recommendation system aims at suggesting properties/hotels to a user such that they would prefer the recommended property over others.
In today’s data-driven world, it would be nearly impossible to follow the traditional heuristic approach to recommend millions of users an item that they would actually like and prefer.
Hence, a Recommendation System solves our problem where it incorporates user’s input, historical interaction, and sometimes even user’s demographics to build an intelligent model to provide recommendations.
In this blog, we will cover all the steps that are required to build a Hotel Recommendation System for the problem statement mentioned…
There was a time when we considered traditional marketing practices, and the successes or failures they yield, as an art form. With mysterious, untraceable results, marketing efforts lacked transparency and were widely regarded as being born out of the creative talents of star marketing professionals, but the dynamics switched, and regime of analytics came into power. It has evolved over the time and numerous methodologies have been discovered in this regard. Market mix model is one among those popular methods.
The key purpose of a Marketing Mix Model is to understand how various marketing activities are contributing together in driving…
Gartner’s report reveals that around 37% of businesses across the industries are leveraging AI and ML technologies in some form to scale up the business. To back this report — it’s predicted that 80% of modern technologies use in an IT organization will be complemented by AI and ML by 2022.
AI and ML undoubtedly keep disrupting the new technology adoption in 2021 that would certainly change the way we think, live, and work in the near future. Hard to believe this if this is real? Think of IBM’s Chef Watson; it has analyzed 9,000-odd recipes in the Bon…