• Online Seminar on Mathematical Foundations of Data Science
  A weekly online seminar on random topics on mathematical foundations of machine learning, statistics and optimization...

• Don’t Fear the Robot
  I invented Roomba and assure you, robots won’t take over the world...I’ve been inventing autonomous machines for over 30 years and one of them, Roomba from iRobot, is quite popular. During my career, I’ve learned a lot about what makes robots valuable, and formed some strong opinions about what we can expect from them in the future. I can also tell you why, contrary to popular apocalyptic Hollywood images, robots won’t be taking over the world anytime soon...

• Explainable Deep Learning: A Field Guide for the Uninitiated
  A practitioner beginning to study explainable deep learning may be intimidated by the plethora of orthogonal directions the field is taking...To alleviate this problem, this article offers a "field guide" to deep learning explainability for those uninitiated in the field. The field guide: i) Discusses the traits of a deep learning system that researchers enhance in explainability research, ii) places explainability in the context of other related deep learning research areas, and iii) introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning. The guide is designed as an easy-to-digest starting point for those just embarking in the field...

Data scientists are in demand on Vettery

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• A visual explanation for regularization of linear models
  Linear and logistic regression models are important because they are interpretable, fast, and form the basis of deep learning neural networks. They are also extremely simple; we're just fitting lines (or hyperplanes) through training data...What's lacking is a simple and intuitive explanation for what exactly is going on during regularization. The goal of this article is to explain how regularization behaves visually, dispelling some myths and answering important questions along the way...

• Data Science Job Search Tips During COVID-19
  With my eyes barely open, my alarm blaring away, I frantically opened the email that had the results of an on-site interview I had given a few days ago...“We thank you for taking the time to apply to our data science…..”, I had read enough. The devastation and hopelessness that had consumed my half-awaken body at that instant had drained away my energy to read the rest...

• Problems Data Scientists face in their jobs [Reddit Discussion]
  It is two years old article, which I came across the two-year-old article "Why so many data scientists are leaving their jobs"..It was quite a successful article (48K claps)...But I got a negative opinion about the article. I mean, you can walk away, get another job, and then repeat. Sure. But why not understand the other side of story? Why not see what are the problems, figure out the cause, and fix them...Is it just me who thinks like that? Is it my bias based on what I have seen (and may be misinterpreting the article)? I want to get a sense of what community thinks...

• Deep Reinforcement Learning Works - Now What?
  Two years ago, Alex Irpan wrote a post about why “Deep Reinforcement Learning Doesn’t Work Yet”. Since then, we have made huge algorithmic advances, tackling most of the problems raised by Alex...Despite these advances, I argue that we, as a community, need to re-think several aspects. Below I highlight several fundamental problems, present some works which I believe are in the right direction and try to offer some plausible solutions...

• Consistent Video Depth Estimation: Generating HQ Depth Maps From Single Video Input
  Whether on a robot gripper in a high-tech factory or onboard an autonomous vehicle navigating busy city streets, accurate depth estimation is an essential element of computer vision systems across a wide range of tasks and applications. Performing accurate 3D scene reconstruction from image sequences is a problem that has been studied in the computer vision community for decades...To take advantage of ubiquitous smartphone videos while overcoming challenges in this and other existing approaches — such as missing regions in the depth maps or inconsistent geometry and flickering depth — researchers from the University of Washington, Virginia Tech and Facebook have introduced an algorithm that can reconstruct dense, geometrically consistent depth for all pixels in monocular videos...

• Towards understanding glasses with graph neural networks
  A deeper understanding of glasses may lead to practical advances in the future, but their mysterious properties also raise many fundamental research questions. Though humans have been making silica glasses for at least four thousand years, they remain enigmatic to scientists: there are many unknowns about the underlying physical correlates...Our new work, published in Nature Physics, could help us gain an understanding of the structural changes that may occur near the glass transition. More practically, this research could lead to insights about the mechanical constraints of glasses (e.g., where a glass will break)...

• Using Neural Networks to Find Answers in Tables
  Much of the world’s information is stored in the form of tables, which can be found on the web or in databases and documents. These might include anything from technical specifications of consumer products to financial and country development statistics, sports results and much more. Currently, one needs to manually look at these tables to find the answer to a question or rely on a service that gives answers to specific questions (e.g., about sports results). This information would be much more accessible and useful if it could be queried through natural language...

• Graph Representation Learning for Algorithmic Reasoning [Video]
  I'm Petar, a Research Scientist at DeepMind, and I have published some works recently on core graph representation learning, primarily using graph neural nets (GNNs)...I've recently given an invited talk at WWW'20 which concisely summarises this research direction, (hopefully) gives some motivation for getting in on the action (especially if you're a GNN person!), and outlines the three recent publications in the area (which, amazingly, attack this problem from three rather distinct angles)...

• My First Year as a Freelance AI Engineer
  This week marks my one-year anniversary of quitting my full-time job and becoming an independent NLP/ML engineer and researcher (which I just call “freelance AI engineer” below). My experience so far has been very positive, and the past year was probably one of the most productive years in my entire career...A number of friends and people that I know asked what it’s like to be a freelancer. Many of them haven’t even heard of any “freelance researchers” before (yeah, me neither). That’s why I’m writing down my thoughts and experience here so that this might be useful if you are even vaguely interested...

Quick Question For You: Do you want a Data Science job?

After helping hundred of readers like you get Data Science jobs, we've distilled all the real-world-tested advice into a self-directed course.
The course is broken down into three guides:

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!

Click here to learn more
...
*Sponsored post. If you want to be featured here, or as our main sponsor, contact us!

• Data Scientist - Amazon Demand Forecasting - New York
  
  The Amazon Demand Forecasting team seeks a Data Scientist with strong analytical and communication skills to join our team. We develop sophisticated algorithms that involve learning from large amounts of data, such as prices, promotions, similar products, and a product's attributes, in order to forecast the demand of over 190 million products world-wide. These forecasts are used to automatically order more than $200 million worth of inventory weekly, establish labor plans for tens of thousands of employees, and predict the company's financial performance. The work is complex and important to Amazon. With better forecasts we drive down supply chain costs, enabling the offer of lower prices and better in-stock selection for our customers...

        Want to post a job here? Email us for details >> team@datascienceweekly.org

• Learn Convolutional Neural Network (CNN) in your browser!
  In CNN Explainer, you can see how a simple CNN can be used for image classification. Because of the network’s simplicity, its performance isn’t perfect, but that’s okay! The network architecture, Tiny VGG, used in CNN Explainer contains many of the same layers and operations used in state-of-the-art CNNs today, but on a smaller scale. This way, it will be easier to understand getting started...

• Calculating Streaks in Pandas
  A streak is when several events happen in a row consecutively. In this post, we’re going to be working with NBA shot data and looking at players who made or missed a number of shots in a row. That said, streaks can take many forms. You can just as easily use this technique to detect and measure other streaks like consecutive days logging in to an app or website...This tutorial assumes you have some basic familiarity with the pandas and Matplotlib libraries...

• Web Scraping with R
  Want to scrape the web with R? You’re at the right place!..We will teach you from ground up on how to scrape the web with R, and will take you through fundamentals of web scraping (with examples from R)...Throughout this article, we won’t just take you through prominent R libraries like rvest and Rcrawler, but will also walk you through how to scrape information with barebones code...Overall, here’s what you are going to learn: 1) R web scraping fundamentals, 2) Handling different web scraping scenarios with R, 3) Leveraging rvest and Rcrawler to carry out web scraping...

• Data Science in Production: Building Scalable Model Pipelines with Python
  This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production....
  For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page
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  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