The 5 Sampling Algorithms every Data Scientist need to know by @MLWhiz via @Medium

Here’s an intro to common sampling techniques.

Includes some sample Python code which makes it really easy to incorporate into your code (with some editing). Make sure you follow Rahul and give him lots of applause for helping you with this.

Open-endedness: The last grand challenge you’ve never heard of by Kenneth O. Stanley Joel Lehman and Lisa Soros via @OReillyMedia

While open-endedness could be a force for discovering intelligence, it could also be a component of AI itself.

This is a little bit of a long read but is worth the investment in time. A very interesting concept that I found fascinating. Something to think about.

China’s AI brain drain by Karen Hao via @techreview

China has pushed—successfully—to increase the number of Chinese AI researchers. But a new analysis shows that although the number of Chinese AI researchers has increased tenfold over the last decade, the majority of them live outside the country.

Impressive that they have a strategy but it could offer problems to organisations outside of China as they tempt people back to work in AI.

7 trends impacting commercial and industrial IoT data by Sastry Malladi via @infomgmt

Here's a look at seven top trends that are driving this space, from compute size to the value of true edge computing, to closed-loop edge to cloud machine learning.

Interesting article and very useful - worth a bookmark if you have any intention to use IOT.

No Time To Read AI Research? Topbots have summarised Top Papers From The Past Year by Mariya Yao via @topbots

Trying to keep up with AI research papers can feel like an exercise in futility given how quickly the industry moves. If you’re buried in papers to read that you haven’t quite gotten around to, you’re in luck. To help you catch up, Topbots have summarised 10 important AI research papers from 2018 to give you a broad overview of machine learning advancements in the past year.

This is incredibly useful and well worth using this important resource and saving yourself some valuable time.

Pay attention to the man behind the curtain by @quaesita via @topbots

Google’s chief decision scientist, Cassie Kozyrkov, talks about AI bias and how to handle it.

Great observations and well worth reading and thinking about in relation to your own work and the quality of what you are doing.

What 70% of Data Science Learners Do Wrong by Dan Becker via @kdnuggets

Lessons learned from repeatedly smashing his head with a 2-meter long metal pole for a college engineering course.

You definitely need to bookmark this article and make sure you can always get back to it for reference.

Why big data analytics is crucial to how the IoT works and grows by Savaram Ravindra via @informgmt

Big data is the fuel of IoT and artificial intelligence that drives the connected things is its brain.

good article that is worth reading and thinking about it a while if you want to implement anything with IoT you need to pay attention to analytics to get the full value from the investment.

The Google Cloud Developer’s Cheat Sheet by/via @gregsramblings

Every product in the Google Cloud family described in <=4 words (with liberal use of hyphens and slashes)

A great resource via his Github library.

Deep learning is about to get easier by Ben Dickson via @VentureBeat

One problem with deep learning algorithms is that they require vast amounts of data. Fortunately, researchers have found workarounds that will level the playing field.

This is really interesting and anything that helps more people take advantage of AI has got to be a great thing (if it has been tested to make sure you can rely on the answers).

3 strategies for working with data in R by Alex Gold via @rstudio

"For many R users, it’s obvious why you’d want to use R with big data, but not so obvious how." Here's how.

Loved this well thought out article. Definitely worth a bookmark to save it somewhere for later/next time.

All hail the algorithm by @Hey_AliRae via @AJEnglish

Al-Jazeera has published a five-part video series exploring the impact of algorithms on our everyday lives.

An interesting series and not something I would have associated with this channel.

Lack of digital standards making data management increasingly complicated by Bob Violino via @infomgmt

With no international alignment on how to regulate the digital environment, organizations are managing an increasingly complicated set of conflicting rules in key markets.

This is definitely going to get worse as I spent much of my working life creating standard data models and mapping data from systems to the data model. Not always easy to achieve data available and easy to understand.

WEBINAR: Why Data Prep is Step 1 for Analytics Success - 6th June 2019

Data Science Central Webinar Series Event

Why Data Prep is Step 1 for Analytics Success Join us for the latest DSC Webinar on August 6th, 2019 Sad but true when it comes to data prep - data practitioners spend up to 80% of their time scrubbing and preparing their data before performing any meaningful analytics. Meanwhile, organizations are increasingly moving data and analytics from the on-premise environment to the cloud as part of their digital transformation initiatives. The rapid migration to the cloud further extends this data preparation nightmare given the varying shapes and sizes of the data stored in the cloud. Join David Menninger, SVP & Research Director at Ventana Research, and Jie Wu, Product Marketing Director at Trifacta for a live discussion on how to select a cloud data preparation solution to accelerate your analytics journey in the cloud. In this latest Data Science Central webinar, you will learn: Challenges with data prep in the cloud Why ETL tools alone are not sufficient to deliver well-prepared data in the cloud Key considerations when selecting a data prep tool for cloud data lakes and cloud data warehouses Featured Speakers: Jie Wu, Product Marketing Director -- Trifacta David Menninger, SVP & Research Director -- Ventana Research Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central   Title:  Why Data Prep is Step 1 for Analytics Success Date:  Tuesday, August 6th, 2019 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

WEBINAR: Industry Trends in Digital Transformation, AI & Data Literacy 6-7 August 2019

Sponsored News from Data Science Central

WEBINAR DIGITAL TRANSFORMATION WEBINAR SERIES August 6 – 7, 2019 REGISTER       Unlock the mysteries of Digital Transformation. Industry trends. AI. Data literacy. And analytics strategy. It’s time to peel back the mysteries surrounding data-driven Digital Transformation and uncover the truth about delivering value. On August 6 and 7, join us for The DX Files – a webinar series exploring how the most successful organizations are using data to transform operations, processes, and outcomes. You’ll get the big picture from James Staten of Forrester, and analytics experts from Accenture, KPMG, The School District of Philadelphia, Deloitte, and Qlik® will reveal how they’re unlocking insights. Topics include: Industry trends setting the stage for data use The role of analytics in driving transformation Using AI to surface new and better insights Boosting data literacy for a competitive edge Get bold ideas for using data to reinvent – and meet the future head-on. Register now to reserve your spot.     August 6 – 7, 2019 REGISTER

Register here

WEBINAR: Modernizing Legacy ERP Analytics with Data Prep on AWS 15 August 2019

Data Science Central Webinar Series Event

Modernizing Legacy ERP Analytics with Data Prep on AWS Join us for the latest DSC Webinar on August 15th, 2019 There are difficulties any time a role is left vacant within an organization. The difficulties are compounded when others have struggled with taking on the responsibilities because the person who previously filled the role had institutional knowledge that isn’t widely available. This is a common scenario faced by many organizations and it causes roadblocks in the reporting processes. However, B/A Products was able to overcome this by leveraging their data preparation solution to help modernize their legacy ERP systems. B/A Products has been able to cut a 6-12 month process of reformatting, restructuring and preparing data down to 2 months. Complex pattern matching and parsing of unstructured date that required lots of time an effort in hand coding has now been simplified and automated. Because of the natural language format of the workflows, anyone can easily come into the organization and take over reporting. This latest Data Science Central webinar will help you to: Understand technology trends that simplify your analytics modernization journey Learn about the challenges and solutions that B/A Products used to solve their issues with legacy ERP systems Accelerate time-to-value for analytics projects with data preparation on AWS See in action the before / after with the solution live demo Featured Speakers: Jacob S J Joseph, Information Systems Manager -- B/A Products Co. Samantha Winters, Director of Marketing and Business Analytics -- B/A Products Co. Matt Derda, Customer Marketing Manager -- Trifacta Hosted by: Stephanie Glen, Editorial Director -- Data Science Central   Title:  Modernizing Legacy ERP Analytics with Data Prep on AWS Date:  Thursday, August 15th, 2019 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat no

Google AI Blog:Predicting the Generalisation Gap in Deep Neural Networks by Yiding Jiang via @googleai

Here’s a description of a new technique that uses margin distributions to better predict a DNN’s generalization gap.

Seems a great idea to use what the Google AI team have made available in their Github is a great idea and should not be ignored. Links to a lot of sources are given thought the article.