This paper presents a machine learning based approach for solving the business problem of identifying from pictures the products chosen by the Kaufland customers. These pictures are all taken from the same angle and typically show one or multiple products from the same category in a bag which makes the background and the bag recurrent elements.
Here we explored the method of transfer learning – using already trained and very deep NN like InceptionV3, InceptionResnetV2, VGG19, Resnet50 with combinations of retraining and no retraining of the existing layers. We solved this multiclassification problem of predicting the probabilities of each class of products by adding different final custom layers and we obtain the best result of 85% accuracy on a validation set of 20% (which was never seen by the training model).
This result was achieved with the model VGG19 which distinguished itself not only for providing the best categorical accuracy but also for training speed, execution speed once deployed and reduced resource consumption.
The objective of our task is extract parent-subsidiary relationship in text. For example, a news from techcruch says this, ‘Remember those rumors a few weeks ago that Google was looking to acquire the plug-and-play security camera company, Dropcam? Yep. It just happened.’. Now from this sentence we can infer that Dropcam is a subsidiary of Google. But there are million of companies and several million articles talking about them. A Human being can be tired of doing even 10! Trust me 😉 We have developed some cool Machine learning models spanning from classical algorithms to Deep Neural network do this for you. There is a bonus! We just do not give you probabilities. We also give out that sentences that triggered the algorithm to make the inference! For instance when it says Orcale Corp is the parent of Microsys it can also return that the sentence in its corpus ‘Oracle Corp’s Microsys customer support portal was seen communicating with a server’, triggered its prediction.
Kaufland-Case 1. Business Understanding Industrial vibration analysis is a measurement tool used to identify, predict, and prevent failures. Implementing vibration analysis on the machines will improve the reliability of the machines and lead to better machine efficiency and reduced down time eliminating mechanical or electrical failures. Vibration analysis are used to identify faults in machinery, plan machinery […]
Cryptocurrencies are a type of digital currencies that, since their creation, have become a global phenomenon known to most people. Our job is to build a machine learning algorithm able to forecast their price based on a set of given features such as that currency’s price, market cap, circulating supply etc.
1. Business Problem Formulation The current political landscape is shaped by extreme polarization of opinions and by the proliferation of fake news. For example, a recent study published in Science has found that rumors and fake news tend to spread six times faster than truthful information. This situation both damages the reputation of respectable news outlets and […]
Techonnology and methods used: R – plyr, dplyr, tidyverse, stringr, data.table, geohash, ggmap, maps, robustbase, geosphere, pracma, Hmisc, ggplot2, tidyquant, reshape2, pastecs Python – s3fs, pandas, numpy, matplotlib, plotly, geohash2, folium, geopy OLS Regression, Ridge Regression, Decision Trees Introduction Air pollution beyond the norms is a common problem in many locations. Examining the causes behind and being able to predict […]
Datathon – Enthusiast Team
We developed workflow utilizing Blast and Centrifuge toolkits, that is able to provide precise metagenomics information about food composition, from comparing DNA reads with reference genomes of various species. Our workflow is optimized to work on Google Cloud instance (Compute Engine) with 24 CPUs and 200 GB of RAM.
1. Business Understanding A Kaufland store is a very big thing. It has a sales floor of up to 12.000 square meters and provides more than 30.000 products. A lot of events can occur on our shelves that are likely to be overlooked. Items can get sold out, other items might be placed on the […]
Artificial Intelligence (AI) is making huge impact in the Logistics Industry. Recent research shows that AI will enable companies to “…derive between $1.3trn and $2trn a year in economic value from using AI in supply chain…”.
Following this spirit Kaufland has prepared a hot case straight out of their Supply Chain Management systems.
Case Summary A Kaufland store is a very big thing. It has a sales floor of up to 12.000 square meters and provides more than 30.000 products. A lot of events can occur on our shelves that are likely to be overlooked. Items can get sold out, other items might be placed on the wrong […]