11 ways novices can start the process of learning AI programming by @YEC via @thenextweb

Artificial intelligence systems represent a pretty exciting area of study: There is a good-sized call for people with the skills needed, and the technology is still developing and growing. However, it can be difficult to figure out how best to get involved with the tech, especially if you’re wanting to learn on your own. Here are 11 ways identified by a panel at the Young Entrepreneur Council.

This is a great list that everyone who uses AI (or wants to use it) needs to review and use to builda a personal development plan.

Which Data Science Skills are core and which are hot/emerging ones? by Gregory Piatetsky, via @kdnuggets

They have identified two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.

This should be very useful for anyone who is already working in or wants to be working in Data Science. Great diagrams too.

Bloomberg will move historic trading data to cloud in support of clients’ machine-learning ambitions by Caroline Donnelly via @computerweekly

Bloomberg will migrate its historical market data to the cloud so that the company’s clients can use the data to test algorithms and train machine learning models. Bloomberg’s chief information officer, Tony McManus, said the company had a growing number of hedge fund clients requesting access to its databases for data analytics purposes.

This is great news for any company developing anything in the area of company or trading data.  So much better than any on-premise data as it can be more up-to-date.

An AI system identified a potential new drug in just 46 days by Charlotte Jee via @techreview

Using general adversarial networks (GANs) and reinforcement learning, researchers at the University of Toronto working with AI startup Insilico Medicine, created 30,000 designs for molecules that target a protein linked with fibrosis (tissue scarring) in 21 days. Six of these molecules were synthesized in the lab, two of those were tested in cells, and the most promising one was tested in mice. The researchers concluded the mouse-tested molecule was potent against the protein and showed “drug-like” qualities. The entire process took just 46 days.

Just wow - thi is some great progress and I just want this to be used against all sorts of illnesses to make some amazing progress. This is definitely a technique that requires further investigation and experimenting so it can be used.

Facebook, Carnegie Mellon build first AI that beats pros in 6-player poker by Noam Brown via @facebookai

AI has never been good at bluffing, but Facebook’s Pluribus poker bot crushed human pros at six-player, no-limit Texas Hold’em. If each chip in the experiment were worth a dollar, Pluribus would have made $1,000 an hour against the pros. This is significant for real-world applications because unlike chess, poker is a hidden- or imperfect-information game—as are most real-world problems.

This was fascinating to me and well worth a read - you can also find some interesting stuff if you look on their website and could apply some of the techniques or disciplines to your own work.

TensorFlow ML framework for graphical data released by @TensorFlow via @Medium

TensorFlow released Neural Structured Learning (NSL), an open-source framework that uses the neural graph learning method for training neural networks with graphs and structured data.

This is a great article and has some code to make sense and so use your Medium account and give them some applause and a follow.

WEBINAR: Migrating R Applications to the Cloud using Databricks - 2 September 2019

Data Science Central Webinar Series Event

Migrating R Applications to the Cloud using Databricks Join us for the latest DSC Webinar on September 26th, 2019 R, along with Python, is the most popular language among enterprise data scientists. The R ecosystem includes thousands of packages for statistical analysis and machine learning as well as advanced graphical capabilities. R users across enterprises are expressing strong interest in leveraging cloud for R workloads. Cloud offers several unique advantages, such as accessing ever-growing datasets, easily scaling up compute resources for processing large data, managing resources more cost efficiently. In this latest Data Science Central webinar, we will demonstrate how Databricks helps R users migrating their applications from legacy on-prem environments to public clouds such as AWS and Azure: Seamless migration of models developed in desktop RStudio to RStudio in Databricks Leverage Databricks Notebooks with MLflow to enhance their work Recording and notebooks will be provided after the webinar so that you can practice at your own pace. Speaker: Hossein Falaki, Tech Lead -- Databricks Hosted by: Stephanie Glen, Editorial Director -- Data Science Central Title:  Migrating R Applications to the Cloud using Databricks Date:  Thursday, September 26th, 2019 Time:  09:00 AM - 10:00 AM PDT Space is limited so please register early: Reserve your Webinar seat now

How new tools in data and AI are being used in health care and medicine by Ben Lorica and Mike Loukides via @OReillyMedia

This is an overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data.

I love this deep dive into a whole raft of uses for AI in data cleaning, exploring and the different types of training described in the article. Lots of links to other articles to look at areas in more detail and worth a bookmark.

WEBINAR: Some Great Ways to Visualise Survey Data (and Virtually any Type of Data) - 24 September 2018

Data Science Central Webinar Series Event

Some Great Ways to Visualize Survey Data Join us for this latest DSC Webinar on September 24th, 2019 Steve Wexler’s datarevelations.com website is one of the premier resources for visualizing survey data with Tableau. As Steve points out on the site, the number one impediment to success with Tableau is getting your data “just so.” In this latest Data Science Central webinar, Steve will first preview Tableau Prep, Tableau’s data preparation tool and show you how to wrangle cumbersome survey data. Next, Steve will show two different, but insanely useful, techniques for visualizing check-all-that-apply questions and benchmarking questions. This should be a great combination of visualization best practices mixed with “get-your-hands-dirty” in Tableau. Speaker: Steve Wexler, Founder -- Data Revelations Hosted by: Stephanie Glen, Editorial Director -- Data Science Central Title:  Some Great Ways to Visualize Survey Data (and Virtually any Type of Data) Date:  Tuesday, September 24th, 2019 Time:  9:00 AM - 10:00 AM PDT Space is limited so please register early: Reserve your Webinar seat now

Overcoming the five most common data analytics challenges by Christopher Roberts via @Infomgmt

To optimize your business, you must accumulate and analyze the data and feedback you’ve been getting from all aspects of your business. Here are the solutions to top challenges.

A very useful article that I think deserves a read and bookmark.

What happened to Hadoop? by @derrickharris via @Medium

It was the next big thing...until it wasn’t. Derrick Harris explains, “Hadoop’s path to ubiquity intersected a host of other technology shifts that as a whole would prove to be more impactful in the long run, in part by peeling off the most valuable promises of big data and making them more consumable.”

Definitely, a question that needed to be answered!  Thank you Derrick for the great answer - to thank him give him applause and a follow.

What’s next for the popular programming language R? by Dan Kopf via @qz

Hadley Wickham discusses R and where it’s going, tidy evaluation, and the different cultures of R and Python users and how their viewpoints differ.

Any R user will find this really interesting. I have to agree with him that there is no "competition" between R and Python - it's more about what is best for what you are doing, what you are comfortable using or a combination of the two. I like R and I am better at R than Python (which I struggle with at times). That's just me, and I'm sure others are the other way around.

Top Deep Learning Frameworks of 2019 and How Do They Compare by Gaurav Belani via @AiThority

This post looks at five deep learning platforms and offers the pros and cons of each.

I really like that the pros and cons are given for the 5 on this list. I'm not sure I could choose between them although I think I would want to go nearer to Keras than TensorFlow but that is more of personal preference and not necessarily something you should take notice of.

9 top trends that are driving AI and software investments by Laurent Bride via @infomgmt

IT and data leaders are constantly challenged to keep up with new trends in emerging and disruptive technologies and to determine how each can best aid the organization.

Definitely, something to review your strategy and training plans against when preparing for the future.

WEBINAR: - How to Use Time Series Data to Forecast at Scale - 12 Sept 2019

Data Science Central Webinar Series Event

How to Use Time Series Data to Forecast at Scale Join us for this latest DSC Webinar on September 12th, 2019 The growing popularity of sensor networks and telemetry applications has lead to the collection of a vast amount of time-series data, which enables forecasting for a multitude of use cases from application performance optimization to workload anomaly detection. The challenge is to automate a historically manual process handcrafted for the analysis of a single data series of just tens of data points to large scale processing of thousands of time series and millions of data points. In this latest Data Science Central webinar, we will demonstrate how to leverage InfluxDB to implement some solutions to tackle on the issues of time series forecasting at scale, including continuous accuracy evaluation and algorithm hyperparameters optimization. As a real-world use case, we will discuss the storage forecasting implementation in Veritas Predictive Insights which is capable of training, evaluating and forecasting over 70,000 time series daily. Speaker: Marcello Tomasini, Sr. Data Scientist -- Veritas Technologies Hosted by: Rafael Knuth, Contributing Editor -- Data Science Central Title:  How to Use Time Series Data to Forecast at Scale Date:  Thursday, September 12th, 2019 Time:  9:00 AM - 10:00 AM PDT Space is limited so please register early: Reserve your Webinar seat now

7 Reasons Your Data Science Resume is Suboptimal by/via @odsc

Seven reasons your resume could be failing you.

Something for us all to look into no matter our experience level.