My top 3’s on a bunch of different topics. I hope future me isn’t too judgemental. Top Engineering Podcasts: Python Bytes Software Engineering Daily Changelog Network Top Linux Podcasts: Jupiter Broadcasting Network Ubuntu Podcast Late Night Linux Top Data Science Podcasts: Data Engineering Podcast Data Framed Linear Digressions Top Beers: Great Lakes Commodore Perry IPA Thirsty Dog 12 Dogs of Christmas Rhinegeist Brewery Truth IPA Top Cleveland East Side Parks:
TLDR; Data Engineering is the most important job title in your company that should have head count zero. Hint: just hire more Data Scientists. My thoughts on Data Engineering Please take everything said here as an opinion piece meant to be thought provoking. Definition: in this article “data engineer” is referring to the “ETL” consultant to the Data Science Team. If you’re a data engineer building data inrfastructure and reusable software components to empower the data science team, then please keep doing that.
JosiahNet: live coding a GOLANG Deep Learning Library SourceCode: github.com/thenomemac/josiahnet Inspired by Joel Grus’s youtube live coding of a deep learning frameworks, I wondered as a very new to GOLANG user could I live code a deep learning framework? It ended up taking a few hours to code this up as I didn’t have numpy as a starting point, but I found GOLANG to be very suitable for implementing a Deep Learning Library in Go with no dependencies.
TLDR; Train a neural net to identify midwestern fish specifes and create an interactive web app identify invasive specicies from user photos. whatismyfish.net is currectly in a mid-refactor state as I’m re-writing the python/flask web app in golang for fun and profit. Project Summary: The mobile web app WhatIsMyFish.net has been awarded 3rd place in the semi-final round of Eriehack.io : a first of its kind regional, eco focused engineering challenge.
This tutorial walks users through an entire deep learning based NLP pipeline. The notebook demos a sentiment model for IMDB movie reviews using an LSTM and benchmarks the model against a linear model with tokenized TFIDF features. The LSTM model is then introspected by looking at works most likely to flip a review from positive to negative and vice-versa. Then TSNE is used to visualize the the distribution of the most impactful words in movie reviews.