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Deepfakes are the result of a generative adversarial network (GAN) and unsupervised machine learning. After getting initial data, the computer teaches itself henceforth, using a Generator and a Discriminator. The generator creates the initial fake image, audio, or video and sends it over to the Discriminator for examination. If it determines the image is fake, … Read moreWhat is a Deepfake and Why Should You Care?

Cleaning text for your NLP projects has never been so fun and easy! I like working with textual data. As for Computer Vision, in NLP nowadays there are a lot of ready accessible resources and opensource projects, which we can directly download or consume. Some of them are realy cool and permit us to speed … Read moreAutomate Data Cleaning with Unsupervised Learning

Exploratory Data Analysis of Instacart orders via Saturn Cloud As a Data Scientist, a big part of my responsibility is sharing my results with the business stakeholders. As a Data Journalist, a big part of my role is sharing code chunks with my readers. Now the reports that I share need to be rendered properly … Read moreInstacart Market Basket Analysis Part 1: Which Grocery Items Are Popular?

Many researchers and developers are interested in what Neural Architecture Search can offer their Deep Learning models, but are deterred by monstrous computational costs. Many techniques have been developed to promote more efficient search, notably Differentiable Architecture Search, parameter sharing, predictive termination, and hierarchical representations of architectures. This article will explain the idea of hierarchical … Read moreHierarchical Neural Architecture Search

Part III: Where causal inference stands in the current AI, Big Data, Data Science, Statistics, and Machine Learning scene? This is the third part of the post “What to expect from a causal inference business project: an executive’s guide”. You will find the second one here. Most of these words have fuzzy meaning, at least … Read moreWhat to expect from a causal inference business project: an executive’s guide III

Part II: Which are the project key points you need to know This is the second part of the post “What to expect from a causal inference business project: an executive’s guide”. You will find the third part here. Casual inference models how variables affect each other. Based on this information, uses some calculation tools … Read moreWhat to expect from a causal inference business project: an executive’s guide II

Part I: When do you need casual inference? This is the fifth post on a series about causal inference and data science. The previous one was “Solving Simpson’s Paradox”. You will find the second part of this post here. Causal inference is a new language to model causality to help understand better causes and impacts … Read moreWhat to expect from a causal inference business project: an executive’s guide I

Reinforcement Learning using Temporal Difference Learning In this article I will cover Temporal-Difference Learning methods. Temporal-Difference(TD) method is a blend of Monte Carlo (MC) method and Dynamic Programming (DP) method. Below are key characteristics of Monte Carlo (MC) method: There is no model (agent does not know state MDP transitions) agent learn from sampled experience … Read moreTemporal-Difference learning

Reinforcement Learning using Monte Carlo Method In this article I will cover Monte Carlo Method of reinforcement learning. I have briefly covered Dynamic programming (Value Iteration and Policy Iteration) method in earlier article. In Dynamic programming we need a model(agent knows the MDP transition and rewards) and agent does planning (once model is available agent … Read moreMonte Carlo Learning

According to recent research of PWC, 72% of business leaders said they believe AI is going to be fundamental in the future and they termed it a “business advantage”. No doubt, AI is one of the most crucial future technology which is being opted by many businesses — small or big — rapidly. 12 Best … Read more12 Best AI & ML Based App Ideas that’ll Make Money in 2020

My first attempt at Lesson 2 of “Practical Deep Learning for Coders” by fast.ai I’m a math adjunct and aspiring data scientist working through the “Practical Deep Learning for Coders” course by fast.ai (you can read about my experience with Lesson 1 here), and for Lesson 2, we’re to gather a set of images from … Read moreClassifying pregnancy test results

And avoiding the common mistakes that derail most test efforts. This article is the 3rd one in my series of articles about A/B Testing. In the first article, I presented the intuition behind A/B testing and the importance of establishing the magnitude of the effect you hope to observe and corresponding sample size. In the … Read moreA guide for selecting an appropriate metric for your A/B test

Call centers have been revolutionized in the past decade. While some static call scripts and one-size-fits-all strategies still remain, technology has drastically changed the way call centers are capable of functioning. Today, call centers have the unique ability to leverage all available data to drive each customer interaction. These data sources include which digital marketing … Read more5 Best Practices for AI- and Data-Driven Call Centers

Automation has uprooted countless things, and it’ll do more of the same in the future. Data science is one of the things automation changed. Whether you’re already working in the field or identify as an aspiring data scientist, knowing about the changes relevant to automation and data science mentioned here will help you prepare for … Read more4 Ways Automation Is Altering Data Science

Types and sources of data bias The huge success of applications of machine learning (ML) applications in the past decade — in image recognition, recommendation systems, e-commerce and online advertising — has inspired its adoption in domains such as social justice, employment screening, smart interactive interfaces such as Siri, Alexa, and the like. Along with … Read moreUnderstanding Data Bias

How to build a highly customized AI framework for content moderation, using state-of-the-arts in deep learning. Authors: Shervin Minaee, Harsh Pathak, Thomas Crook As an online travel agency website, images of a property (e.g. hotel, resorts, apartment, vacation rental) are invaluable references for travel shoppers considering which property they want to book. Expedia Group™️ receives … Read moreMachine Learning Powered Content Moderation: Computer Vision Applications at Expedia

Hi everyone, today’s topic will be about streamlining your machine learning models with sklearn, xgboost, and the h2o package. In particular, we will examine predicting bankruptcies of Polish companies using their financial statements. In my early days of machine learning modeling, I always wondered if there was an easier way to tune models. From that … Read moreStreamline Model Tuning on Bankruptcy Predictions

A general understanding of what self-driving cars are really about. Lyft’s self-driving car [Source] Every year, there are around 1.25 million deaths caused by road accidents. That’s equivalent to 3,287 deaths on a daily basis! As a teenager just learning how to drive, this is a scary fact that lingers at the back of my … Read moreAn Introduction to Autonomous Vehicles

Left: cosine similarity of U1 to all other users; Right: weighted average of ratings for I3 So our predicted rating for U1 and I3 is 4.34! Also note, different similarity metrics would give slightly different results. Item-item collaborative filtering is pretty much the same as user-user, but instead of computing the similarity between users, similarity … Read moreMath for Data Science: Collaborative Filtering on Utility Matrices

Providing an alternative look at the most looked at show The crew (source) FRIENDS is one of my favourite shows (probably the favourite) and I’m sure I’m not alone in having rewatched the entire series more than once. I’ve always wondered if there was anything left to know about this oh-so familiar group. After seeing … Read moreThe One with all the FRIENDS Analysis

Check out how Business Intelligence (BI) and data analytics remove uncertainty in business and provide insights that help in decision making and forecasting. Business Intelligence and data analytics are an integral part of any successful business venture. Business analytics has its dedicated market in the industry and is often a sought-after method to skip the … Read moreHow Data Analytics is Helping Small Businesses Re-Imagine Growth Opportunities

This is the second of a series of posts on recommendation algorithms in python. In the first of the series, that I wrote quite a while ago, I quickly went through a number of algorithms that I implemented and tried using Kaggle’s Ponpare dataset. You can find all the related code in the repo. In … Read moreRecoTour II: neural recommendation algorithms

Are you thinking to build a machine learning project and stuck between choosing the right programming language for your project? Well, then this article is going to help you clear the doubts related to the characteristics of Python and R. Let’s get started with the basics. R and Python both share similar features and are … Read morePython Vs R: What’s Best for Machine Learning

Taking my Android model on a tour through the city of Siem Reap Ta Prohm. Siem Reap, Cambodia. Photo by me. What’s better than a machine learning model? A deployed one. A model that’s actively producing something. The opposite of it is what I like to call “PowerPoint AI,” models that only live inside a … Read moreObject Detection in the City

Co-author: Maarit Widmann With this post I am going back to the classics. Here explained the confusion matrix and some accuracy measures associated with it. — — — — — — — A classification model assigns data to two or more classes. Sometimes, detecting one or the other class is equally important and bears no … Read moreConfusion Matrix and Class Statistics

A step-by-step guide for Landsat 5 multispectral data classification Deep Learning has taken over the majority of fields in solving complex problems, and the geospatial field is no exception. The title of the article interests you and hence, I hope that you are familiar with satellite datasets; for now, Landsat 5 TM. Little knowledge of … Read moreNeural Network for Satellite Data Classification Using Tensorflow in Python

Recognising Cinematic Shot Types with a ResNet-50 Originally published at https://rsomani95.github.io.Visit the link for a better formatted, interactive version of the post with many more images. GitHub: https://github.com/rsomani95/shot-type-classifier –What is Visual Language, and Why Does it Matter? –Neural Networks 101 (Read if you don’t know what neural networks are) –The Dataset — — Data Sources … Read moreA.I. For Filmmaking

Most service-based companies suffer from a phobia of the word “Product”. So service-based companies develop POCs instead. Let’s first understand what is what: Product is an article or substance that is manufactured or refined for sale. Proof of Concept (POC) is a miniature representation of the end-product, with a few working features, that aims of … Read moreAce Deep Learning in a Service-Based Organization

Survival analysis is a “censored regression” where the goal is to learn time-to-event function. This is similar to the common regression analysis where data-points are uncensored. Time-to-event modeling is critical for understanding users/companies behaviors not limited to credit, cancer, and attrition risks. Cox-Proportional Hazard model is a semi-parametric model where we model hazard ratio using … Read moreSurvival Modeling — Accelerated Failure Time — Xgboost

The next best thing to changing the past — aggregating it. A technical introduction to the pandas resample function. This article is an introductory dive into the technical aspects of the pandas resample function for datetime manipulation. I hope it serves as a readable source of documentation for those less inclined to digging through the … Read moreUsing the Pandas “Resample” Function

Introduction Wine selection is complicated and personal. The one you love doesn’t necessarily depend on the rating or price. It depends on your personal taste. As a wine lover, I am always overwhelmed by the vast amount of options at wine stores or restaurants . What if you can have a virtual personal sommelier who … Read moreText Analytics: Wine Classification and Recommendation by Tasting Notes

Whether you like it or not, guest reviews are becoming a prominent factor affecting people’s bookings/purchases. Think about your past experience. When you were looking for a place to stay for a vacation on Expedia/Booking/TripAdvisor, what did you do? I am willing to bet you’d be scrolling down the screen to check on the reviews … Read moreSentiment Analysis for Hotel Reviews

Editor’s note: This is the fourth episode of the Towards Data Science podcast “Climbing the Data Science Ladder” series, hosted by Jeremie Harris, Edouard Harris and Russell Pollari. Together, they run a data science mentorship startup called SharpestMinds. You can listen to the podcast below: If you’ve been following developments in data science over the … Read moreThe thin line between data science and data engineering

Let’s say your friend asks you for a movie recommendation. How do you go about doing this? If you’re not too into movies and if it’s not a friend you know too well, perhaps you Google best movie on netflix and give your friend the answers to whatever listicle you find. Maybe your friend tells … Read moreA Primer to Recommendation Engines

Building a simulator to analyze maple syrup coffee trends Photo by Mohamed Hassan Who doesn’t love maple syrup? Most people, actually, because they have never had the opportunity to taste it. Canada’s province of Québec produces 70% of the world’s maple syrup supply. In the time that I have spent living in Montreal, I have … Read moreCoffee Trends in NYC

Linear Regression is the first stepping stone in the field of Machine Learning. If you are new in Machine Learning or a math geek and want to know all the math behind Linear Regression, then you are at the same spot as I was 9 months ago. Here we will look at the math of … Read moreEverything You Need To Know About Linear Regression

Impulse Response Functions generated by me using the statsmodels library in python Interpreting VECMs In a previous article, I created a model to predict the closing share prices of FANG stocks using a vector error correction model (VECM), which models cointegrated time series. I touched on some topics I wanted to expand on, namely interpreting … Read moreLong-Run Relationships between FANG Stocks

Natural language processing (NLP) is a sub-field of machine learning (ML) that deals with natural language, often in the form of text, which is itself composed of smaller units like words and characters. Dealing with text data is problematic, since our computers, scripts and machine learning models can’t read and understand text in any human … Read moreWhy do we use word embeddings in NLP?

Security concerns have stalled enterprise AI/ML on the cloud. Identity-based security addresses the concerns. Here is what you need to know. Why has enterprise AI/ML on the cloud stalled? Mostly, because of valid security concerns. But the march for AI/Machine Learning on the cloud is inevitable. And AI/ML’s voracious appetite for data — much of … Read moreWhy Identity Management is a Prerequisite for Enterprise AI-ML on the Cloud

A k-means clustering of top 100 artists on Spotify The sound of music has evolved with society over the years, thus the innovation in sound is really a reflection of our cultural and technological progression. Music that is relevant to the mainstream population now would’ve been impossible to create a couple of years ago because … Read moreWhat Data Science can tell us about mainstream music…

Because your time is more important than the machine When we create our machine learning models, a common task that falls on us is how to tune them. People end up taking different manual approaches. Some of them work, and some don’t, and a lot of time is spent in anticipation and running the code … Read moreAutomate Hyperparameter Tuning for your models

Graphs are applicable to many real-world datasets such as social networks, citation networks, chemical graphs, etc. The growing interest in graph-structured data increases the number of researches in graph neural networks. Variational autoencoders (VAEs) embodied the success of variational Bayesian methods in deep learning and have inspired a wide range of ongoing researches. Variational graph … Read moreTutorial on Variational Graph Auto-Encoders

Photo by Sai Kiran Anagani on Unsplash There’re many online articles guiding the users how to build their own deep learning box, like https://blog.slavv.com/the-1700-great-deep-learning-box-assembly-setup-and-benchmarks-148c5ebe6415https://medium.com/adventures-in-high-density/the-3700-deep-learning-box-purchase-assembly-and-setup-458900680eb0 They showed what computer parts you should purchase and also how to assemble it, and the remaining setup. But for a newbie like me, it’s still a lot of trouble to … Read moreA newbie’s guide to build your own deep learning box