Open sourcing

We, the tech community thrive on open source.

When we use open source projects, we do not ask ourselves:
1. What kind of education contributors of the project have?
2. Can the contributors balance a binary tree or implement an LRU cache?
3. Can the open source folks design twitter?
We just take an open source project, and use it.

Why, when it comes to interviewing and hiring, we forget about it and jump into colonist mentality of looking for top of the class, most experienced, most this and most that talent? Why we forget to focus on the talent who gets the job done?

Everyday, we use open source software written possibly by the people who lack a lot of privileges, just to hire people who are privileged all around.

Notes on the book: Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists by Alice Zheng and Amanda Casari

Recently finished reading

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists ( link to Amazon)
by Alice Zheng and Amanda Casari.

Great coverage of the topic, that is often omitted in Machine Learning intro books, or books on ML infrastructure.
Picked up tons of programmatic tricks on how to deal with numerical and categorical data. There are good examples in Python.

Several spots in the book are in need of more attention or re-work, imho.
The code printouts were quite long, and were hard to follow on Kindle or iPad.

Also, the appendix section on linear modeling and linear algebra doesn’t seem to belong. And it can be part of the Feature Engineering topic, but in a more intuitive and down to earth way – the coverage seemed a bit more abstract that it could benefit the reader.

Informative error messages are great.

Looks like beloved Gabriel José de la Concordia García Márquez would NOT be able to change his address on the

Thank you USPS for the error message that doesn’t make any sense to people with Last names that are several distinct words.

{field: "lastName", message: "The Last Name field allows only letters and the following characters ( - )."}

Apache MXNet 1.1 on Windows 10 64 bit

Attempted to run MXNet 1.1 GPU version on Windows 10. It works well, but the process of installation has a few details that is helpful to know.
1. You can create Anaconda environment based on Python 3.6 and use environment’s pip to install GPU version of Apache MXNet on Windows – `pip install mxnet-cu90` at the moment. I believe cu91 is in the works and soon will be available.
2. You still need to download and install CUDA 9.0 as well as CuDNN 7.1 (FOR WINDOWS 10, NOT WINDOWS 7 – very easy to mistake them!) from the nVidia website. If CUDA itself is available for the download without an account – to get CuDNN library you need to have a free account on the nVidia page.
3. You still need to install latest graphics card drivers on the system, even after you installed all the CUDA stuff. For me MXNet was breaking on initialization of GPU context until I’ve rolled GPU drivers.

A few helpful links:
Cuda 9.0 download –
CuDNN download – ( I’ve tested 7.1 version and it works well)
nVidia GPU drivers –