Data Science Articles & Videos

• The mathematics of adversarial attacks in AI
  It is well established that the current DL methodology produces universally unstable neural networks (NNs). The instability problem has caused an enormous research effort -- with a vast literature on so-called adversarial attacks -- yet there has been no solution to the problem. Our paper addresses why there has been no solution to the problem, as we prove the following mathematical paradox: any training procedure based on training neural networks for classification problems with a fixed architecture will yield neural networks that are either inaccurate or unstable (if accurate) -- despite the provable existence of both accurate and stable neural networks for the same classification problems...

• Parallelizing Python Code
  Python is great for tasks like training machine learning models...When performing these tasks, you also want to use your underlying hardware as much as possible for quick results. Parallelizing Python code enables this. However, using the standard CPython implementation means you cannot fully use the underlying hardware because of the global interpreter lock (GIL) that prevents running the bytecode from multiple threads simultaneously...This article reviews some common options for parallelizing Python code...

• Using learning-to-rank to precisely locate where to deliver packages
  For delivery drivers, finding the doorstep where a package should be dropped off can be surprisingly hard. House numbers can be obscured by foliage, or they might be missing entirely; some neighborhoods use haphazard numbering systems that make house numbers hard to guess; and complexes of multiple buildings sometimes share a single street address...I adapt an idea from information retrieval — learning-to-rank — to the problem of predicting the coordinates of a delivery location from past GPS data...

• Building a smart Robot AI using Hugging Face 🤗 and Unity
  Today we’re going to build this adorable smart robot that will perform actions based on player text input...It uses a deep language model to understand any text input and find the most appropriate action of its list...What’s interesting with that system, contrary to classical game development, is that you don’t need to hard-code every interaction. Instead, you use a language model that selects what’s robot possible action is the most appropriate given user input...

• Bayesian Media Mix Modeling for Marketing Optimization
  A problem faced by many companies is how to allocate marketing budgets across different media channels. For example, how should funds be allocated across TV, radio, social media, direct mail, or daily deals?...So-called Media Mix Modelling (MMM) can estimate how effective each advertising channel is in gaining new customers. Once we have estimated each channel’s effectiveness we can optimize our budget allocation to maximize customer acquisition and sales...In this blog post, we outline what you can do with MMM’s, introduce how they work, summarise some of the benefits they can provide, as well as covering some of the modeling challenges...

• Bad Labels: GridSearch is Not Enough
  I write a lot of blog posts on why you need more than grid-search to properly judge a machine learning model. In this blog post I want to demonstrate yet another reason; labels often seem to be wrong...The issue here isn’t just that we might have bad labels in our training set, the issue is that it appears in the validation set. If a machine learning model can become state of the art by squeezing another 0.5% out of a validation set one has to wonder. Are we really making a better model? Or are we creating a model that is better able to overfit on the bad labels?...

• Anomaly Detection: Why Your Data Team Is Just Not That Into It
  Here’s why and how some of the best data teams are turning to DevOps and Site Reliability Engineering for inspiration when it comes to achieving a proactive, iterative model for data trust. Introducing: the Data Reliability lifecycle...

• bad labels: introduction
  Even famous datasets have bad labels in them...Because it's such a big problem we wanted to spend a few videos on this topic. It'd be a shame if our machine learning models are merely optimal because they overfit on the bad labels. That's why we're going to explore heuristics to find bad labels in our training data so that we may try to improve the quality of our training data...

• Embedding Values in Artificial Intelligence (AI) Systems
  Though there are numerous high-level normative frameworks, it is still quite unclear how or whether values can be implemented in AI systems. Van de Poel and Kroes’s (2014) have recently provided an account of how to embed values in technology. The current article proposes to expand that view to complex AI systems and explain how values can be embedded in technological systems that are “autonomous, interactive, and adaptive”...

• How To Lead In Data Science
  The Data Exchange Podcast: Jike Chong and Yue Cathy Chang on helping data scientists increase their impact in business and in society...

Training*

1. Data Science Getting Started Guide. This guide shows you how to figure out the knowledge gaps that MUST be closed in order for you to become a data scientist quickly and effectively (as well as the ones you can ignore)


2. Data Science Project Portfolio Guide. This guide teaches you how to start, structure, and develop your data science portfolio with the right goals and direction so that you are a hiring manager's dream candidate


3. Data Science Resume Guide. This guide shows how to make your resume promote your best parts, what to leave out, how to tailor it to each job you want, as well as how to make your cover letter so good it can't be ignored!