About Brandon Rohrer

Brandon Rohrer is Principal Data Scientist at iRobot, specializing in predictive modeling of complex systems, algorithm design, and general purpose machine learning.

Brandon Rohrer Posts (18)

  • How to Use Python’s datetime - 17 Jun 2019
    Python's datetime package is a convenient set of tools for working with dates and times. With just the five tricks that I’m about to show you, you can handle most of your datetime processing needs.
  • Choosing an Error Function - 10 Jun 2019
    The error function expresses how much we care about a deviation of a certain size. The choice of error function depends entirely on how our model will be used.
  • Separating signal from noise - 04 Jun 2019
    When we are building a model, we are making the assumption that our data has two parts, signal and noise. Signal is the real pattern, the repeatable process that we hope to capture and describe. The noise is everything else that gets in the way of that.
  • Choosing Between Model Candidates - 29 May 2019
    Models are useful because they allow us to generalize from one situation to another. When we use a model, we’re working under the assumption that there is some underlying pattern we want to measure, but it has some error on top of it.
  • End-to-End Machine Learning: Making videos from images - 23 May 2019
    Video is a natural way for us to understand three dimensional and time varying information. Read this short post on how to achieve the creation of videos from still images.
  • Silver BlogPandas DataFrame Indexing - 29 Apr 2019
    The goal of this post is identify a single strategy for pulling data from a DataFrame using the Pandas Python library that is straightforward to interpret and produces reliable results.
  • How Optimization Works - 18 Apr 2019
    Optimization problems are naturally described in terms of costs - money, time, resources - rather than benefits. In math it's convenient to make all your problems look the same before you work out a solution, so that you can just solve it the one time.
  • How to Survive Your Data Science Interview - 01 Mar 2018
    There are many wonderful things about data science. It’s extreme breadth is not one of them. The title of data scientist means something different at every company
  • Gold BlogData Science and the Imposter Syndrome - 15 Sep 2017
    You are not the only one who wonders how much longer they can get away with pretending to be a data scientist. You are not the only one who has nightmares about being laughed out of your next interview.
  • Silver Blog, March 2017How to Get a Data Science Job: A Ridiculously Specific Guide - 07 Mar 2017
    Job hunting is challenging and sometimes frustrating task and we all experience it in our career. Here we provide a very specific and practical guide to get your dream job in Data Science world.

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