Introduction to Quantum Programming

Walkthrough of quantum programming from qubits to running real quantum programs! Quantum Computer — Courtesy of Rigetti Quantum computers exist! And so does quantum programming! In this article, I’ll walk you through everything you need to know to get started with quantum programming. I’ll start off with some context about how quantum computers differ from … Read more Introduction to Quantum Programming

Introduction to Stream Processing

Together with machine learning and serverless stream processing seems to be one of the hottest topics nowadays. Companies are onboarding modern stream processing tools, service providers are releasing better and more powerful stream processing products, and specialists are in high demand. This article introduces the basics of stream processing. It starts with a rationale for … Read more Introduction to Stream Processing

Game of Thrones Season 8: What went wrong?

Using Data Science to investigate what made the final season of the hit TV show so unpopular Few TV series have caused as much drama amongst viewers as Game of Thrones. For the first several seasons fans were adamant that the show was unrivalled in its storytelling, gripping drama and loveable characters. Fast forward to … Read more Game of Thrones Season 8: What went wrong?

The Inspection Paradox is Everywhere

The inspection paradox is a statistical illusion you’ve probably never heard of. It’s a common source of confusion, an occasional cause of error, and an opportunity for clever experimental design. And once you know about it, you see it everywhere. One of my favorite examples is the apparent paradox of class sizes. Suppose you ask … Read more The Inspection Paradox is Everywhere

Hands-on Global Model Interpretation

Example and Interpretation What feature does a model think are important for determining if a patient has or doesn’t have a heart disease? This question can be answered using feature importance. As I already mentioned at the start of the article we will work on the Heart Disease Data-set. You can find all the code … Read more Hands-on Global Model Interpretation

AI Justice: When AI Principles Are Not Enough

Şerife Wong, Faith and Trust, 2019, collage of images found during research Fluxus Landscape is an art and research project mapping about 500 stakeholders and actors in AI ethics and governance. It casts a broad net and each included stakeholder defines artificial intelligence and ethics in their own terms. Together, they create a snapshot of … Read more AI Justice: When AI Principles Are Not Enough

How To Write Clean Code That Reduces Headaches.

What is clean code and why do we need it? Abstraction is evil. Code is anti-evil, and clean code is perhaps divine. Robert Martin nailed it to perfection when he said. “The only valid measurement of code quality is What-The-F**ks/Minute.” Let me explain a bit further. Whenever I do a code review, my mind churns … Read more How To Write Clean Code That Reduces Headaches.

What goes up must come down

The meaning of regression to the mean Photo by JC Dela Cuesta on Unsplash In 1933, statistician Horace Secrist published a book titled The Triumph of Mediocrity in Business, in which he made a bold claim: over time, market competition causes the top-performing businesses to become worse, and the worst-performing businesses to become better, resulting … Read more What goes up must come down

Understanding the Fundamentals of Linear Regression

How this common algorithm functions Imagine for a moment that you live in a city with a problem. The gardeners in your city have recently taken to putting garden gnomes in their gardens, and they’re mind-bogglingly ugly. The gardeners have even started having a competition around it, seeing who can place the biggest gnome in … Read more Understanding the Fundamentals of Linear Regression

Singular Value Decomposition Example In Python

Singular Value Decomposition, or SVD, has a wide array of applications. These include dimensionality reduction, image compression, and denoising data. In essence, SVD states that a matrix can be represented as the product of three other matrices. In mathematical terms, SVD can be written as follows: where n is the number of rows (i.e. samples) … Read more Singular Value Decomposition Example In Python

Bayesian Modeling Airlines Customer Service Twitter Response Time

Student’s t-distribution, Poisson distribution, Negative Binomial distribution, Hierarchical modeling and Regression Twitter conducted a study recently in the US, found that customers were willing to pay nearly $20 more to travel with an airline that had responded to their tweet in under six minutes. When they got a response over 67 minutes after their tweet, … Read more Bayesian Modeling Airlines Customer Service Twitter Response Time

The rise of the machine learning engineer

The machine learning engineer is the profession missing to take us to the AI future. Over the years with every technological shift, new opportunities emerged and a new type of profession was needed to fulfill the new needs. These changes had a significant impact on how companies used to work, and right now we are … Read more The rise of the machine learning engineer

Clustering & Visualizing Travelers’ Stories with Doc2Vec and WebGL

In Depth Analysis This post delves into the results from an Interactive Visualization of thousands of travel stories and their connections. The goal of the visualization is to make the submissions of aspiring writers fun to discover. It is intended for a wide audience of users; whether it be aspiring travel writers, daydreaming office workers … Read more Clustering & Visualizing Travelers’ Stories with Doc2Vec and WebGL

Turn your previous Python projects into awesome tools — with Tkinter

Where are the buttons?! Yes, it’s time to get the buttons to show up in the app. We already took care of the function to exit the app. The command is called “destroy” which sounds super creepy and scary, but that’s what it does: it destroys the window! So, our close_app function is a single … Read more Turn your previous Python projects into awesome tools — with Tkinter

How to beat resistance to AI projects: 3 steps

What’s holding back adoption of AI in your organization? A real-life example of how a pharma company overcame 3 common obstacles. In a recent survey by O’Reilly, respondents reported “company culture” to be the biggest bottleneck for artificial intelligence adoption in their organizations. In spite of the buzz around AI, most people in organizations don’t … Read more How to beat resistance to AI projects: 3 steps

The Dawn of a New Era for People Analytics

Supercharging People Analytics with Graph Technology Author: J Hope For so many companies, it seems, People Data is somewhat of an untapped resource. Yet to overcome the biggest strategic challenges in Human Capital Management facing companies today— developing, using and retaining talent, improving performance, enabling growth-mindset and developing Agile working cultures — being able to … Read more The Dawn of a New Era for People Analytics

Stop Using Zip Codes for Geospatial Analysis

The last time you used your zip code, you were most likely entering your address into a website to make a purchase, finding a store near your home or office, or filling out some other online form. You likely found the answer you were looking for and didn’t stop to think further about that five-digit … Read more Stop Using Zip Codes for Geospatial Analysis

Most People Screw Up Multiple Percent Changes. Here’s How to Get Them Right.

Let’s work through examples representing these 4 situations: 2 increases: suppose the battery life of a smartphone goes up 40% in one year and 10% in the next. What’s the total change? 2. 2 decreases: suppose the town tax rate decreases by 60% and then by 40% (we can only wish). If we add the … Read more Most People Screw Up Multiple Percent Changes. Here’s How to Get Them Right.

Advances on interpretability of deep Neural Nets at ICIAM 2019

An introduction to different methods for Interpretability can be found here. During the ICIAM Theoretical advances of deep learning mini-symposia, there were some talks on interpretability, perhaps the most interesting ones were by Wojciech Samek, Fraunhofer Heinrich Hertz Institute, and by Stephan Waeldchen, Technische Universität Berlin. The first talk debated how LRP can be understood … Read more Advances on interpretability of deep Neural Nets at ICIAM 2019

How to power up your product by machine learning with python microservice, pt. 1

The main motivation is to show how to conduct data science and machine learning projects and give a hands-on walk through on how to build and integrate machine learning service into software product with microservice oriented architecture. You may find these articles useful, if you are software engineer working on machine learning project, or data … Read more How to power up your product by machine learning with python microservice, pt. 1

Pyspider —A Practical Usage on Competitor Monitoring Metrics

Scripting — Part 2 @config(age=10 * 24 * 60 * 60)def index_page(self, response):print(response.cookies)self.crawl(‘https://www.similarweb.com/website/google.com’, fetch_type = ‘chrome’,validate_cert = False,headers = self.headers,cookies = response.cookies,callback=self.index_page_1) In our situation, the competitor we are interested in is google.com. That’s why the url is https://www.similarweb.com/website/google.com. You will notice there is another decorator called config, this decorator is to indicate that if … Read more Pyspider —A Practical Usage on Competitor Monitoring Metrics

Location-Based Recommendations

Create a location-based recommender with machine learning. Location-based recommendation — Source Microsoft. Recommendation systems are widely utilized in different applications for predicting the preference or rating of a user in a product or service. Most likely you have come across or interacted with some type of recommender systems in the past few minutes or hours … Read more Location-Based Recommendations

How to Build a Trading Bot in 5 Easy Steps

Step #1: Fail Fast Jump straight into complex machine learning algorithms without having a clue about the maths behind them. Oooo whats this shiny thing called A.I.? You’re telling me I can just copy and paste these 100 lines of code and I’ll be able to tell the future? AWESOME! I spent weeks trying ready … Read more How to Build a Trading Bot in 5 Easy Steps

Gaining a solid understanding of Pandas series.

A series is the backbone of DataFrame Series is the backbone of DataFrame The two central data structures of Pandas are Series and DataFrame. This post is an attempt to have a proper understanding of Pandas series. The foundation of a DataFrame is a Series. The docstring of DataFrame defines a DataFrame as: Can be … Read more Gaining a solid understanding of Pandas series.

Basic Feature Engineering to Reach More Efficient Machine Learning

If I ask you a question like does machine learning difficult, you might obviously say yes. But machine learning is not a difficult task. Because every algorithm you might need is pre implemented and available as packages (mostly in python libraries). You can import whatever library you want and then start building the model. But … Read more Basic Feature Engineering to Reach More Efficient Machine Learning

A Comprehensive Guide to Correlational Neural Network with Keras

From Theory to implementation Human being along with many other animals have 5 basic senses: Sight, Hearing, Taste, Smell, and Touch. We also have additional senses like a sense of balance and acceleration, sense of time, etc. Every single moment human brain processes information from all these sources and each of these senses affect our … Read more A Comprehensive Guide to Correlational Neural Network with Keras

Is The Data Science Profession At Risk of Automation?

This is the part that is probably easier to automate. Assuming that we successfully obtained and cleaned all our data (not easy to do), we are now ready to build the model. While I would argue that an experienced data scientist or statistician would be an invaluable expert in picking the right model and correctly … Read more Is The Data Science Profession At Risk of Automation?

Shifts & Twists in Business Analytics: Reflections from Qlik Qonnections and Alteryx Inspire

Like driving up a mountain pass, Business Analytics evolves mostly upward, but with unexpected shifts in thinking and sudden twists in technology. In this article, two recent events provide clues about the shifts & twists, plus when we might summit this mountain pass. An echo from my young son rings in my mind, “Daddy, are … Read more Shifts & Twists in Business Analytics:
Reflections from Qlik Qonnections and Alteryx Inspire

Neural Networks as universal function approximators

How to intuitvely understand what Neural Networks are trying to do When you first learn about Neural Networks you are bombarded with matrix multiplications, non-linearities, and back propagation. There are many great resources, where you can learn about this (very important) stuff. This is not one of them. The question I want to answer is … Read more Neural Networks as universal function approximators

Linear Discriminant Analysis In Python

Linear Discriminant Analysis (LDA) is a dimensionality reduction technique. As the name implies dimensionality reduction techniques reduce the number of dimensions (i.e. variables) in a dataset while retaining as much information as possible. For instance, suppose that we plotted the relationship between two variables where each color represent a different class. If we’d like to … Read more Linear Discriminant Analysis In Python

How to Build an Automated Trading System using R

Photo by M. B. M. on Unsplash For all R zealots, we know that we can build any data product very efficiently using R. An automated trading system is not an exception. Whether you are doing high-frequency trading, day trading, swing trading, or even value investing, you can use R to build a trading robot … Read more How to Build an Automated Trading System using R

Math and intuition behind Affinity Propagation

Similarity matrix(S) gives us information about the similarity between any instances, in our case between any two people. S(i , j)= -||xi-xj||² Similarity, in our case, is defined as the negative of the euclidean distance between the two instances. The greater the distance between any two instances, smaller is the similarity between them. So we … Read more Math and intuition behind Affinity Propagation

Professor turned Data Scientist: Why Dr. Guido Maretto left academia for the startup life

What is your professional background? After getting my Ph.D at Caltech (California Institute of Technology), I was a post-doctorate in Brussels in Belgium, and then I started a faculty position in Lisbon in Portugal at the Nova School of Business, where I stayed for 7 years until I moved to industry and became a Data … Read more Professor turned Data Scientist: Why Dr. Guido Maretto left academia for the startup life

LSTM-based African Language Classification

Tired of German-French dataset? Look at Yemba, and stand out. Mechanics of LSTM, GRU explained and applied, with powerful visuals and code in Tensorflow. We gently explain how LSTM cells work, by walking you through a funny example. You will understand why Long Short Term Memory (LSTM) has been so effective and popular for processing … Read more LSTM-based African Language Classification

Transparency in Government AI

A Misplaced Optimism in Transparency in Machine Learning There has also been a renewed sense of agency through the discussion of transparency of machine learning after the passage and implementation of the General Data Protection Regulation (GDPR) in the EU. However, as scholars have pointed out the promises of the “right to explanation” are over … Read more Transparency in Government AI

Trivial Multi-Node Training With Pytorch-Lightning

Synced Training To train the PTL model across multiple-nodes just set the number of nodes in the trainer: If you create the appropriate SLURM submit script and run this file, your model will train on 80 GPUs. Remember, the original model you coded IS STILL THE SAME. The underlying model has no knowledge of the … Read more Trivial Multi-Node Training With Pytorch-Lightning

Coding Interview Questions-Reservoir Sampling

Reservoir Sampling is an algorithm for sampling elements from a stream of data. Imagine you are given a really large stream of data elements, for example: Queries on DuckDuckGo searches in June Products bought at Sainsbury’s during the Christmas season Names in the white pages guide. Let me put in these easy words imagine the … Read more Coding Interview Questions-Reservoir Sampling

Probability: classical, frequentist and subjective approach

Probability can be defined as a tool to manage uncertainty. Whenever an event is neither the certain one (with probability=1) nor the impossible one (probability=0), we are facing an uncertain situation, hence we need to attribute our event a likelihood of occurrence, which is, indeed, probability. In this article, I’m going to present the three … Read more Probability: classical, frequentist and subjective approach

6 Techniques Which Help Me Study Machine Learning Five Days Per Week

I quit Apple. Started a web startup, it failed. My heart wasn’t in it. I wanted to learn machine learning. It got me excited. I was going to learn it all. I wouldn’t need to program all the rules, the machine would learn it for me. But I had no job. Excitement doesn’t pay for … Read more 6 Techniques Which Help Me Study Machine Learning Five Days Per Week

Predicting MLB Game Outcomes with Machine Learning

Beating the Odds in Baseball with XGBoost and LightGBM When the game has started and your model is still training. Image Credit I was terrible at baseball as a kid, always hitting foul balls. My parents eventually figured out I needed eyeglasses, but not before I developed a mild disdain for the sport. That’s me … Read more Predicting MLB Game Outcomes with Machine Learning

No True Effects in High Dimensions

Can we trust results obtained from high-dimensional data? Image source This is the fifth article of the column Mathematical Statistics and Machine Learning for Life Sciences. In this column, I cover what data and analytical challenges we have in Life Sciences such as Biomedicine, Bioinformatics, Cell Biology, Genetics, Evolutionary Biology etc. Perhaps main analytical challenge … Read more No True Effects in High Dimensions

Introduction to Decision Intelligence

A new discipline for leadership in the AI era Curious to know what the psychology of avoiding lions on the savannah has in common with responsible AI leadership and the challenges of designing data warehouses? Welcome to decision intelligence! Source: xijian/Getty Decision intelligence is a new academic discipline concerned with all aspects of selecting between … Read more Introduction to Decision Intelligence

Excel vs SQL: A Conceptual Comparison

Introduction I have been involved in the data analytics realm for about 3 years. I’ve worked in the field for over 2 years as a healthcare analyst, and I recently finished my MBA with a focus in data science. During my masters I was particularly interested in predictive modeling techniques using python (and I still … Read more Excel vs SQL: A Conceptual Comparison