Tim Corey explores Entity Framework, an amazing set of tooling around data access.

With EFCore, that tooling becomes even more powerful. So why is it that I still don’t recommend that people use EFCore?

In this video, he walks you through the best practices of Entity Framework and EFCore and point out the pitfalls to avoid. We will discuss where there are problems and what to do to resolve those problems.

In this episode, Serkant Karaca and Shubha Vijayasarathy from the Azure Event Hubs team talk about how and when to use Azure Event Hubs as the messaging component in our .NET applications. They’ll discuss use cases, cover topics like partitioning  and also show how to use the .NET SDK for Event Hubs.

Useful Links

Siraj Raval has a video exploring a paper about genomics and creating reliable machine learning systems.

Deep learning classifiers make the ladies (and gentlemen) swoon, but they often classify novel data that’s not in the training set incorrectly with high confidence. This has serious real world consequences! In Medicine, this could mean misdiagnosing a patient. In autonomous vehicles, this could mean ignoring a stop sign. Machines are increasingly tasked with making life or death decisions like that, so it’s important that we figure out how to correct this problem! I found a new, relatively obscure yet extremely fascinating paper out of Google Research that tackles this problem head on. In this episode, I’ll explain the work of these researchers, we’ll write some code, do some math, do some visualizations, and by the end I’ll freestyle rap about AI and genomics. I had a lot of fun making this, so I hope you enjoy it!

Likelihood Ratios for Out-of-Distribution Detection paper: https://arxiv.org/pdf/1906.02845.pdf 

The researcher’s code: https://github.com/google-research/google-research/tree/master/genomics_ood

PyOhio posted this great talk by Alice Zhao on NLP in Python.

Natural language processing (NLP) is an exciting branch of artificial intelligence (AI) that allows machines to break down and understand human language. As a data scientist, I often use NLP techniques to interpret text data that I’m working with for my analysis. During this tutorial, I plan to walk through text pre-processing techniques, machine learning techniques and Python libraries for NLP.

Text pre-processing techniques include tokenization, text normalization and data cleaning. Once in a standard format, various machine learning techniques can be applied to better understand the data. This includes using popular modeling techniques to classify emails as spam or not, or to score the sentiment of a tweet on Twitter. Newer, more complex techniques can also be used such as topic modeling, word embeddings or text generation with deep learning.

We will walk through an example in Jupyter Notebook that goes through all of the steps of a text analysis project, using several NLP libraries in Python including NLTK, TextBlob, spaCy and gensim along with the standard machine learning libraries including pandas and scikit-learn.

Setup Instructions

Engadget has a first look at Samsung’s robot chef.

Normally when I miss breakfast, it’s by choice. Today, it was because I was in a rush to get to Samsung’s booth on the CES show floor and see if I could get any face time with the company’s cute new rolling robot. (That, uh, didn’t go so great.) The trip was still well worth it, though, because I got to eat a tofu salad partially made by a pair of robotic arms slung from the bottom of some kitchen cabinets.

Read the full story on Engadget.  

In this special episode, Principal Program Manager, Chris Segura, and Forbes Tech Council member, Mike Walker, talk about what they see as the top blockchain trends for 2020 and what they are hearing from companies deploying solutions.

Trend 1: Practical Blockchain Emerges

  • Blockchain is being leveraged in practical use cases today and are expanding in scope and scale over the next three to five years. This also means creating fit for purpose implementations of blockchain. In some cases breaking norms of permissionless blockchains to shift to permissioned blockchains.

    According to the 2019 Gartner CIO Survey, 60% of CIOs expect some kind of blockchain deployment in the next three years. This is also combined with blockchain will be scalable technically, and will support trusted private transactions with the necessary data confidentiality.

Trend 2: Convergence of the Trinity of Digitization (Blockchain + AI + IoT)

  • Blockchain on its own can provide limited value. Focus on the business solutions where blockchain will provide digital differentiation. Leveraging IoT to reach into the physical and analog world, and AI to provide the orchestration and intelligence to data is a symbiotic

Trend 3: Shift to Digital Ecosystems

  • As blockchain becomes a critical part of an organizations digital business transformation journey, blockchain is increasingly used as a critical enabler of digital ecosystems. This is sometimes referred to also as blockchain consortiums. However, digital ecosystems are much more than a consortium.

Links: GE Aviation Story

Related Links:

Follow @CH9 

Follow @MSFTBlockchain

Geofencing has many practical applications. A geofence is a virtual boundary defining an area on a map. Using tools in Azure Maps, Jim demos how to test if a coordinate is inside or outside the Microsoft Redmond campus boundary.

It can be used to send an alert if an expensive piece of machinery leaves a construction site unexpectedly or to send a warning if a worker enters an unsafe location within a factory.

Jim Bennett shows us how to code geofencing applications with Visual Studio, Python, and Azure Maps.

Jim explains why and how to manage a buffer around your geofence boundary. (Hint: GPS is not that accurate!) He also demos how to set a notification using a web hook and an Azure Logic App when a person or item enters or leaves a geofence area.

Next steps:

Step through the tutorial: Set up a geofence by using Azure Maps

Visit Jim’s blog on geofencing: are you where you should be?

Lex Fridman explains that the best way to understand the mind is to build it in the clip from the opening lecture of the MIT Deep Learning lecture series.

Full video: https://www.youtube.com/watch?v=0VH1Lim8gL8

Website: https://deeplearning.mit.edu

This is a clip from the opening lecture of the MIT Deep Learning lecture series.
Full video: https://www.youtube.com/watch?v=0VH1Lim8gL8
Website: https://deeplearning.mit.edu