25 Must Know Terms & concepts for Beginners in Deep Learning


A curated list of resources dedicated to bayesian deep learning

Data Curation: The Missing Ingredient to Self-service Analytics

In this special guest feature, Stephanie McReynolds, Vice President of Marketing at Alation, discusses how data curation – and more recently, automated curation technology that relies on artificial intelligence (AI) and machine learning algorithms – is emerging as the key, missing ingredient to creating a culture that understands and embraces data. With over 15 years of data infrastructure and application experience, Stephanie has a track record of bringing new technologies to market and into the hands of business analysts. Prior to Alation, she was instrumental in building the first marketing team at the self-service data preparation provider Trifacta. She previously held senior product management positions at a number of data companies including Teradata, Aster Data and Oracle.

insideHPC Special Report Riding the Wave of Machine Learning & Deep Learning

Many companies are moving decisively to develop capabilities based on AI, machine learning and deep learning. In time-honored business fashion, the motivation is a combination of fear and hope. Competitive pressures are spurring companies on, and there is a sense of urgency amongst many enterprise thought leaders about not falling behind. Artificial intelligence (AI) and machine learning-decades-old technologies that are now electrifying the computing industry-for all intents and purposes, seem to be in the process of transforming corporate America. But why is AI so hot right now? Many experts believe it’s because, after 50 years of promises that AI was going to solve critical problems, it’s finally working. AI, machine learning and deep learning are transforming the entire world of technology, but these technologies are only making headway now due to the proliferation of data and the investments being made in storage, compute and analytics solutions. Much of this progress is due to the ability of learning algorithms to spot patterns in larger and larger amounts of data. At first glance, when looking out over the global business landscape, some companies might be considered as “under-investing” in computer systems for AI. Companies first steps should include the “Five-stepenterprise AI strategy”. To learn more about riding the wave of machine learning and deep learning download this insideHPC special report.

March Machine Learning Mania, 1st Place Winner’s Interview: Andrew Landgraf

Kaggle’s 2017 March Machine Learning Mania competition challenged Kagglers to do what millions of sports fans do every year-try to predict the winners and losers of the US men’s college basketball tournament. In this winner’s interview, 1st place winner, Andrew Landgraf, describes how he cleverly analyzed his competition to optimize his luck.

The Path To Learning Artificial Intelligence

Learn how to easily build real-world AI for booming tech, business, pioneering careers and game-level fun.

Simplifying Decision Tree Interpretability with Python & Scikit-learn

This post will look at a few different ways of attempting to simplify decision tree representation and, ultimately, interpretability. All code is in Python, with Scikit-learn being used for the decision tree modeling.

R vs Python: Different similarities and similar differences

A debate about which language is better suited for Datascience, R or Python, can set off diehard fans of these languages into a tizzy. This post tries to look at some of the different similarities and similar differences between these languages. To a large extent the ease or difficulty in learning R or Python is subjective. I have heard that R has a steeper learning curve than Python and also vice versa. This probably depends on the degree of familiarity with the languuge To a large extent both R an Python do the same thing in just slightly different ways and syntaxes. The ease or the difficulty in the R/Python construct’s largely is in the ‘eyes of the beholder’ nay, programmer’ we could say. I include my own experience with the languages below.

Sankey charts for swinging voters

Sankey charts based on individual level survey data are a good way of showing change from election to election. I demonstrate this, via some complications with survey-reweighting and missing data, with the New Zealand Election Study for the 2014 and 2011 elections.

New series: R and big data (concentrating on Spark and sparklyr)

Win-Vector LLC has recently been teaching how to use R with big data through Spark and sparklyr. We have also been helping clients become productive on R/Spark infrastructure through direct consulting and bespoke training. I thought this would be a good time to talk about the power of working with big-data using R, share some hints, and even admit to some of the warts found in this combination of systems. The ability to perform sophisticated analyses and modeling on “big data” with R is rapidly improving, and this is the time for businesses to invest in the technology. Win-Vector can be your key partner in methodology development and training (through our consulting and training practices).

AzureDSVM: a new R package for elastic use of the Azure Data Science Virtual Machine

The Azure Data Science Virtual Machine (DSVM) is a curated VM which provides commonly-used tools and software for data science and machine learning, pre-installed. AzureDSVM is a new R package that enables seamless interaction with the DSVM from a local R session, by providing functions for the following tasks:
1.Deployment, deallocation, deletion of one or multiple DSVMs;
2.Remote execution of local R scripts: compute contexts available in Microsoft R Server can be enabled for enhanced computation efficiency for either a single DSVM or a cluster of DSVMs;
3.Retrieval of cost consumption and total expense spent on using DSVM(s).
AzureDSVM is built upon the AzureSMR package and depends on the same set of R packages such as httr, jsonlite, etc. It requires the same initial set up on Azure Active Directory (for authentication).