WEBINAR: No-code ML for Forecasting and Anomaly Detection 28 May 2020

Data Science Central <mail@newsletter.datasciencecentral.com> Unsubscribe	14 May 2020, 19:09 (5 days ago)
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Data Science Central Webinar Series Event No-code ML for Forecasting and Anomaly Detection Join us for this latest DSC Webinar on May 28th, 2020 In this latest Data Science Central webinar, we will introduce and demonstrate how you can perform common time-series Machine Learning tasks such as Forecasting and Anomaly Detection, directly within the Influx platform without the need to use external tools, languages and services. During this webinar, you will learn: How to initiate Machine Learning tasks directly within the Influx visual interface without intimate knowledge of how these algorithms are implemented How data scientists can wrap existing, or develop new, Machine Learning algorithms for publication to the Influx time-series platform using familiar languages and frameworks Speaker: Dean Sheehan, Field CTO -- InfluxData Hosted by: Stephanie Glen, Editorial Director -- Data Science Central   Title:  No-code ML for Forecasting and Anomaly Detection Date:  Thursday, May 28th, 2020 Time:  9:00 AM - 10:00 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

24 Best (and Free) Books To Understand Machine Learning by Reashikaa Verma, via @ParallelDots

ParallelDots have compiled a list of some of the best (and free) machine learning books that will prove helpful for everyone aspiring to build a career in the field.

Useful to have and some great books in this list too.

WEBINAR: Embracing Responsible AI from Pilot to Production 27 May 2020

Data Science Central Webinar Series Event

Embracing Responsible AI from Pilot to Production Join us for the latest DSC Webinar on May 27th, 2020 On average, 80% of AI projects fail to make it to production. But it IS possible to successfully launch AI, at scale, that is built responsibly and works for everyone. How you scale from pilot to production is critical to ensuring AI success, while continuing to be a good corporate citizen through responsible productization. In this latest Data Science Central webinar, we'll talk about the framework for scaling AI pilots to production with a focus on ethical responsibilities and bias mitigation at each step. We'll look at: The five-step AI development cycle Ways to control for unwanted bias across data, models, and run time at the production layer Explainability and why it is key for moving AI pilots to production that delivers core business value Speakers: Lukas Biewald, Founder & CEO -- Weights & Biases Alyssa Simpson-Rochwerger, VP of AI & Data -- Appen Hosted by: Sean Welch, Host and Producer -- Data Science Central   Title:  Embracing Responsible AI from Pilot to Production Date:  Wednesday, May 27th, 2020 Time:  9 AM - 10 AM PDT   Space is limited so please register early: Reserve your Webinar seat now

Do Not Use “+” to Join Strings in Python by Christopher Tao via @TDataScience

A comparison of the approaches for joining strings in Python, using “+” and join() method.

This is very interesting and I love that it works through examples and doesn't just tell you to use the join() method.

I Have a Data Warehouse, Do I Need a Data Lake Too? by Troy Hiltbrand via @TDWI Transforming

When building your data and analytics program, you must decide whether you need a data warehouse, a data lake, or both. Understanding the difference is the first step.

Important to understand the difference and this is very useful to help you understand it.

Advantages of Data-driven Marketing Over Traditional Marketing by Edward Huskin via @Datafloq

With the technological advances in big data and how we collect, process, and analyze it, marketing as we know it has changed through the years. Data-driven strategies have pushed the envelope when it comes to predicting customer behaviour and adapting approaches accordingly. It allows for the creation of relevant experiences that are tailor-made to address customer demands and expectations.

This is interesting and in the world, we live in today using data to drive your marketing could be the difference between profit or loss and therefore survival of your company. This is definitely becoming an essential and not an option.

Lossless Image Compression through Super-Resolution by Sheng "Scott" Cao via @github

This is the official implementation of SReC in PyTorch. SReC frames lossless compression as a super-resolution problem and applies neural networks to compress images. SReC can achieve state-of-the-art compression rates on large datasets with practical runtimes. Training, compression, and decompression are fully supported and open-sourced.

This is really interesting and very useful. The link is to a paper in Github.

Forecasting s-curves is hard by @clcrozier via @wordpressdotcom

Many things that we think of as exponential functions will actually follow an s-curve. COVID-19 cases, for instance, will eventually become an s-curve because otherwise, the system would reach infinity. This is a short and clear post that shows why s-curves are so hard to model.

I really enjoyed this short blog entry which makes a lot of sense. I'm sure we can all agree with Constance's observations.