Business doesn’t want AI. Business wants results. While we were focused inward on our Advanced Analytic Platforms, smart competitors were rolling up AI/ML with other capabilities into ‘Intelligent Automation’ platforms. This large scale integration of capabilities of which AI/ML is only a part looks a lot like the development of ERPs in the late 90s.
Regular Expression (Regex – often pronounced as ri-je-x or reg-x) is extremely useful while you are about to do Text Analytics or Natural Language Processing. But as much as Regex is useful, it’s also extremely confusing and hard to understand and always require (at least for me) multiple DDGing with click and back to multiple Stack Overflow links.
The one-year anniversary of the implementation of the General Data Protection Regulation (GDPR) has recently passed. Regardless of where you live, during the past year, you’ve probably received your fair share of emails from companies telling you how they’re going to comply with the new regulation and, most likely, asking for your permission to continue using the information they’ve collected about you over the years. If you’re a vendor who’s been collecting information about your customers over the years, the immediate challenge may be more than a mere nuisance. The last thing you need is regulators demanding to know if and how you’re in compliance with the rules; they’re already doing precisely that in Europe.
Over 20% of Amazon’s North American retail revenue can be attributed to customers who first tried to buy the product at a local store but found it out-of-stock, according to IHL group (a global research and advisory firm specializing in technologies for retail and hospitality. In other words, many companies and local stores suck at forecasting. Accurate demand forecasts are necessary if you’re a retailer who has one of their competitors being Amazon. Want to lose business to Amazon? Then produce sh**ty demand forecasts. It is also actually one of the ‘low-hanging fruits’ of a new data science department at a company who’s just getting started on machine learning and AI initiatives. With accurate demand forecasts, you can boost profits by optimizing your labor, prices, and inventory.
RStudio Connect 1.7.6 has been released and is now available for download. This release includes a new publishing method for Git-backed content, the ability for publishers to manage vanity URLs for applications, full support for all SAML authentication providers, and other improvements and bug fixes.
Doc2Vec is an extension of Word2vec that encodes entire documents as opposed to individual words. You can read about Word2Vec in my previous post. Doc2Vec vectors represent the theme or overall meaning of a document. In this case, a document is a sentence, a paragraph, an article, or an essay, etc.
In this article, I will try to explain Word2Vec vector representation, an unsupervised model that learns word embedding from raw text and I will also try to provide a comparison between the classical approach One-hot encoding and Word2Vec.
In my previous blog, we saw a comparative study of XGBoost, LightGBM & Catboost. With that analysis, we were able to conclude that catboost outperformed the other two in terms of both speed and accuracy. In this part, we will dig further into the catboost, exploring the new features that catboost provides for efficient modeling and understanding the hyperparameters. For new readers, catboost is an open-source gradient boosting algorithm developed by Yandex team in 2017. It is a machine learning algorithm which allows users to quickly handle categorical features for a large data set and this differentiates it from XGBoost & LightGBM. Catboost can be used to solve regression, classification and ranking problems.
Now that data science boot camp (Metis) is over, it’s time to study up for interviews. Since I started blogging, I’ve discovered that writing about a concept and attempting to teach it to readers forces me to learn that concept much more deeply. So in the next few weeks, I will be going one by one over all the core tools that every data scientist and aspiring data scientist (like me) should have in their tool belt so that we can all ace our interviews (fingers crossed)! Now on to today’s topic!
Basic knowledge of XLNet to understand the difference between XLNet and BERT intuitively.
Take a peek into the domain of compression, pruning and quantization of state-of-the-art Machine Learning models.
Human Bias in recruitment selection – Case Study I
Deep Learning is one of the biggest breakthroughs in machine learning in the last generation – but does that mean that a generalist data scientist should try to master it?
This tutorial shows you how you can easily implement a Generative Adversarial Network (GAN) in the new TensorFlow Version 2.0. We’ll focus on the basic implementation, which leaves room for optional enhancements. Before we’ll take a closer look at the implementation, we need to understand the idea and theory behind GANs. If you are already familiar with the theory behind GANs you can skip to the implementation part.
As a ‘noble warrior hero’ from the Kree homeworld of Hala, Carol has just discovered that much of what she assumes to be true about herself is a lie. And not just any random fib. Seems the ‘Supreme Intelligence’ – the advanced AI governing the Kree civilization – has been blatantly deceiving her. About her true earthly origins, about the source of her mysterious powers, and about the basis for ongoing war against evil Skrull terrorists. It seems that subterfuge, and treachery, and galactic genocide, are not beneath the ken of a ruling AI composed of the best minds of an advanced, star-hopping civilization. More than a bit distressing. And depressingly familiar to those with a working knowledge of earth’s own history.
All you need to know as a beginner: Most of us probably heard about the success of ResNet, winner of ILSVRC 2015 in image classification, detection, and localization and Winner of MS COCO 2015 detection, and segmentation. It is an enormous architecture with skip connections all over. While using this ResNet as a pre-trained network for my machine learning project, I thought ‘ How can someone come out with such an architecture? ”