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Source: https://en.wikipedia.org/wiki/TensorFlow

Callback Strategies to add incremental benefit and improve Neural Network training

As we know Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data.During the design of a Neural Network, we have countless choices to play with to make the model optimum fit for given data. During my preparation for google TensorFlow developer certification exam, I learned a few cool techniques to improve deep learning model quality. Apart from a few crucial time painstaking n/w design choice choices like no of nodes, no of layers, tons of variable & bias initialization, activation function etc. there are a few…


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A Major challenge for organizations today is reacting and reaching out to right customers at right time using various touch points to improve customer experience and build customer loyalty. Ability to identify right target segment and right solution offerings for promotion campaign is a common business objective in every industry, like Manufacturing, Telecom, BFSI, Retail etc.

Vehicle manufacturers and service providers are struggling to identify the right mix of customers for each vehicle family/type of vehicle(SUV, Sedan,MUV etc.) type and what influences customer to buy vehicle(Driver of Sales) the best, to optimize product/service mix and campaign design. …


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AI in Digital Targeting

In my previous article I discussed about an advanced analytics solution to increase campaign ROI or Return on Marketing Investment (ROMI) thorough propensity modelling techniques. While supervised learning is mostly used (at least so far) method in predictive analytics industry, it has few limitations.

Firstly as supervised learning uses static lists of attributes, which if not refreshed in regular intervals, becomes irrelevant in the course of time. That means these techniques are not prone adaptive learning/self learning (discovering something new intelligence apart from the rule learned during training), i.e. when there is a change in the new data IV(independent variable)…


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Signature Fraud Detection

In my previous article, I discussed advanced analytics application in the area of fraud in a generic fashion. In this article I will delve into details in a specific area of fraud-signature forgery. No wonder that institutions and businesses recognize signatures as the primary way of authenticating transactions. People sign checks, authorize documents and contracts, validate credit card transactions and verify activities through signatures. As the number of signed documents — and their availability — has increased tremendously, so has the growth of signature fraud.

According to recent studies, only check fraud costs banks about $900M per year with 22%…


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Detecting Car Exterior Damage

Recent advances in deep learning and computation infrastructure(cloud,GPUs etc.) have made computer vision applications leap forward: from unlocking office access door with our face to self-driving cars. Not a many years ago image classification task, such as handwritten digit recognition(the great MNIST dataset) or basic object (cat/dog) identification was considered as a great success in the computer vision domain. However Convolutional neural networks (CNN), the driver behind computer vision applications, are fast evolving with advanced and innovative architectures to solve almost any problem under the sun related to the visual system.

Automated detection of car exterior damages and subsequent quantification(damage…


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Recommender System

In industries like e-commerce, retail, news-group or music apps, recommendation system is one of the most important aspects in each of the 5 pillars of customer life cycle- reach,acquisition, Develop/nurture, retention and Retention. Today from e-commerce industry(email/On-site product recommendations)to online advertisement (personalized suggestion with right contents, at right time matching user preferences), Recommender systems are one of the most essential components to influence user online journeys and to gain enhanced customer insight. Therefore it’s not surprising that 35% of Amazon.com’s revenue is generated by its recommendation engine.In …


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Neural Style Transfer

Written: 29th May 2019 by Sourish Dey

In recent few years, we have experienced the application of computer vision in almost every nook and corner of our life — thanks to the availability of huge amounts of data and super-powered GPUs, which have made training and deployment of convolutional neural networks(CNN) super easy. In my previous article, I discussed one such value-added application in business. One of the most interesting discussions today around within machine learning is how it might impact and shape our cultural and artistic production in the next decades. …


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Deep Learning based Automated Feature Engineering

Written: 13th Aug 2018 by Sourish Dey

https://www.linkedin.com/in/sourish-dey-03420b1a/

Importance Feature Engineering:

In my previous article, I discussed the importance of the creation of rich features from the limited number of features. Indeed, the real quality of machine learning/deep learning model comes from extensive feature engineering than from the modeling technique itself. While specific machine learning technique may work best for particular business problem/dataset, features are the universal drivers/critical components for any modeling project. Extracting as much information as possible from the available data sets is crucial to creating an effective solution.

Sourish Dey

Sourish has 9+ years of experience in Data Science, Machine Learning,Business Analytics,Consulting across all domains, currently in Berlin with dunnhumby gmbh.

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