Data Science Weekly Newsletter - Issue 414

Issue #382

Mar 18 2021

Editor Picks
  • Why Computers Will Never Write Good Novels
    You’ve been hoaxed...The hoax seems harmless enough. A few thousand AI researchers have claimed that computers can read and write literature. They’ve alleged that algorithms can unearth the secret formulas of fiction and film. That Bayesian software can map the plots of memoirs and comic books...The hoax, after all, is everywhere: college classrooms, public libraries, quiz games, IBM, Stanford, Oxford, Hollywood...Yet despite all this gaudy credentialing, the hoax is a complete cheat, a total scam, a fiction of the grossest kind. Computers can’t grasp the most lucid haiku. Nor can they pen the clumsiest fairytale. Computers cannot read or write literature at all. And they never, never will...I can prove it to you...
  • Wine & Math: A Model Pairing
    Mention predictive modeling to the general public and you’re likely to conjure memes of complex mathematical equations swirling. Mention wine, and you get a much different reaction. One can be intimidating, the other inviting...In this piece, we’re going to try to close that gap. We’ll build a statistical model trying to predict a wine’s quality by its properties. So grab some liquid courage in your favorite aged grape variety and get ready for MATH...
  • Which color scale to use when visualizing data
    When visualizing data, you’re almost always working with color – e.g., with different hues (red, yellow, blue) for categories or color gradients (light blue, medium blue, dark blue) for maps...If you use them to visualize data, hue palettes and gradients become “color scales.” That’s because they all “map” to some data: For example, every one of your hues stands for a certain category and every color in your gradient stands for a certain value (range)...This article gives you an overview of the different color scales...

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Data Science Articles & Videos

  • Measuring Diversity
    If someone who is not a white man searches for “CEO pictures” and sees a page of white men, they may feel that only white men can be CEOs, further perpetuating lack of representation at companies’ executive levels...Using the careful quantification outlined in a recent paper, Diversity and Inclusion Metrics in Subset Selection, we can quantify biases and push these systems to return a wider range of results...The mathematics of all this is a little easier to follow with abstract shapes. Let’s take a look at some of them...
  • We Failed to Set Up a Data Catalog 3 Times. Here’s Why.
    We worked with a wide variety of data, everything from 600+ government data sources to unstructured data sources like satellite imagery. Our data grew faster than we expected, and we hadn’t really planned how to store or access it beforehand. We quickly realized we needed a central repository to help our team discover, understand, and build trust in all the data sets we were working with...We thought it would be easy enough to figure this out, but we couldn’t have been more wrong. Here’s the story of how it took 4 attempts and 5 years to finally succeed in implementing a successful data catalog for our team...
  • Growing 3D Artefacts and Functional Machines with Neural Cellular Automata
    Neural Cellular Automata (NCAs) have been proven effective in simulating morphogenetic processes, the continuous construction of complex structures from very few starting cells..In this work, we propose an extension of NCAs from 2D to 3D, utilizing 3D convolutions in the proposed neural network architecture. Minecraft is selected as the environment for our automaton since it allows the generation of both static structures and moving machines. We show that despite their simplicity, NCAs are capable of growing complex entities such as castles, apartment blocks, and trees, some of which are composed of over 3,000 blocks. Additionally, when trained for regeneration, the system is able to regrow parts of simple functional machines, significantly expanding the capabilities of simulated morphogenetic systems...
  • Deep Learning for Land Cover Classification of Satellite Imagery Using Python
    This article helps readers to better understand different Deep Learning methods that can be used for land cover classification of Sundarbans satellite data using Python...Remote sensing is the process of detecting and monitoring the physical characteristics of an area by measuring it is reflected and emitted radiation at a distance (typically from satellite or aircraft). Special cameras collect remotely sensed images, which help researchers “sense” things about the Earth...
  • Data Documentation Woes? Here’s a Framework
    In 2016, I was at the helm of a data team that was rapidly scaling...Then...our oldest data team member, someone who’d been with us for two years, told me that he wanted to quit...That incident marked the start of the Assembly Line Project: an effort to make our data team as agile and resilient as possible. Over two years, we created internal tools and frameworks to help our team run better, and we also learned a lot about building a stronger data culture around the principles of self-organization and transparency...In this article, I’ll share the principles and framework we use to organize our own data team...democratize our data, and make documentation a part of our daily workflow...
  • Never a dill moment: Exploiting machine learning pickle files
    Many machine learning (ML) models are Python pickle files under the hood, and it makes sense. The use of pickling conserves memory, enables start-and-stop model training, and makes trained models portable (and, thereby, shareable)...Here, we discuss the underhanded antics that can occur simply from loading an untrusted pickle file or ML model. In the process, we introduce a new tool, Fickling, that can help you reverse engineer, test, and even create malicious pickle files. If you are an ML practitioner, you’ll learn about the security risks inherent in standard ML practices. If you are a security engineer, you’ll learn about a new tool that can help you construct and forensically examine pickle files. Either way, by the end of this article, pickling will hopefully leave a sour taste in your mouth...



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Training & Resources

  • Cookiecutter Data Science
    A logical, reasonably standardized, but flexible project structure for doing and sharing data science work...
  • Kedro-Airflow 0.4.0 — Orchestrating Kedro Pipelines with Airflow
    Kedro is an open-source Python framework for creating reproducible, maintainable, and modular data science code. Its focus is on authoring code and not orchestrating, scheduling and monitoring pipeline runs. We emphasise infrastructure independence, and this is crucial for consultancies...where Kedro was born...Workflows in Airflow are modelled and organised as DAGs, making it a suitable engine to orchestrate and execute a pipeline authored with Kedro. To keep the workflow seamless, we are pleased to unveil the latest version of the Kedro-Airflow plugin, which simplifies deployment of a Kedro project on Airflow...
  • Applied Machine Learning (Cornell Tech CS 5787, Fall 2020) [ 80 videos ]
    Lecture videos and materials from the Applied Machine Learning course at Cornell Tech, taught in Fall 2020...Starting from the very basics, we cover all of the most important ML algorithms and how to apply them in practice...One new idea we tried in this course was to make all the materials executable. The slides are also Jupyter notebooks with programmatically generated figures. Readers can tweak parameters and regenerate the figures themselves...Also, whenever we introduce an important mathematical formula, we implement it in numpy. This helps establish connections between the math and how to apply it in code...



  • Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits

    Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems...

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.

    P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian
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