Here are the latest articles from Microsoft regarding cloud data science products and updates.
- Azure Content Spotlight – What’s New with Cognitive Services – This weeks content spotlight is all about Azure Cognitive Services. Seth Juarez’s AI Show on Channel 9 provides regular updates on all the new AI features on the Azure platform, including Cognitive Services. See below a collection of the latest video’s …[Read More]
- A Scalable End-to-End Anomaly Detection System using Azure Batch AI – This post is authored by Said Bleik, Senior Data Scientist at Microsoft. In a previous post I showed how Batch AI can be used to train many anomaly detection models in parallel for IoT scenarios … several Azure cloud services and Python code that …[Read More]
- Azure.Source – Volume 31 – In addition, Cognitive Services add pre-built, cloud-hosted APIs for developers to add AI capabilities, including new services announced at Build. This post also covers Cognitive Search and Azure Machine Learning (ML) advancements. The Microsoft data …[Read More]
- Azure Stack: the last mile in Hybrid Cloud – These include Microsoft Azure Cognitive Services, exceptionally large HDInsight environments, and Microsoft Azure Data Lake Store. Services which are best consumed in a Hyperscale Cloud will run on Azure, while services that best fit an enterprise …[Read More]
- Using Azure for Machine Learning – I’m interested in learning more about AI, Data Science, and Machine Learning to improve … other interesting and useful products such as Microsoft IoT Hub, SQL Database, and Cognitive Services which I use a lot for Pantrylogs. You can really play …[Read More]
- Use AU Analyzer for faster, lower cost Data Lake Analytics – Do you use Data Lake Analytics and wonder how many Analytics Units your jobs should have been assigned? Do you want to see if your job could consume a little less time or money? The recently-announced AU Analyzer tool can help you today! See our recent …[Read More]
- Simple and robust way to operationalise Spark models on Azure – It gives you everything that Open Source Spark does and then some. I’ve been especially enjoying the effortless ways to move large datasets around and the ease of MLlib for my AI-projects. One of the questions with the simpler models like regressions and …[Read More]
- New AI Services in Azure for students and academics announced at Build 2018 – 1.Object Detection update to custom vision (preview) http://aka.ms/cognitive 2.Video Indexer (Paid Preview) https://azure.microsoft.com/en-us/blog/build-2018-video-indexer-updates/ 1.Bot Builder SDK v4 (preview) Bot Builder homepage or the Bot Builder …[Read More]
- How Azure IoT helped me buy a new house – Part 1 – shares a personal story on how he used Azure IoT to figure out a solution to a problem that many of us face – high electric bills. In the series, Steve shares the process and code that he used to implement this solution. Telemetry data is an important …[Read More]
Here are the latest articles from Microsoft regarding cloud data science products and updates. This week includes IoT hubs, Time Series Insights, Deep Learning Virtual Machine, Python sample code for cognitive services, and more.
Here are the latest articles from Microsoft regarding cloud data science products and updates. Some of the topics this week: Azure ML, AI Research, Intelligent Cloud, and Anomaly Detection.
- What is Azure Machine Learning Studio? – Machine Learning Studio is where data science, predictive analytics … Click Gallery and you’ll be taken to the Azure AI Gallery. The Gallery is a place where a community of data scientists and developers share solutions created using components of …[Read More]
- Four Big Bets For Better AI Research: A Personal Journey – While mining those forums, I discovered a clear problem: people want to write scripts for transforming a column of data, but they don’t know how … heuristics that are today manually programmed in AI systems (and not just our program synthesizers).[Read More]
- Training Many Anomaly Detection Models using Azure Batch AI – This post is authored by Said Bleik, Senior Data Scientist at Microsoft. In the IoT world, it’s not uncommon that you’d want to monitor thousands of devices across different sites to ensure normal behavior. Devices can be as small as microcontrollers …[Read More]
- Satya Nadella email to employees: Embracing our future: Intelligent Cloud and Intelligent Edge – First, computing is more powerful and ubiquitous from the cloud to the edge. Second, AI capabilities are rapidly advancing across perception and cognition fueled by data and knowledge of the world. Third, physical and virtual worlds are coming together to …[Read More]
- Windows 10 RS4 Preview for HoloLens and ONNX offline Machine Learning – Yesterday we released the Windows 10 RS4 preview to HoloLens so this now allows Data scientists and developers creating AI models will be able to deploy their innovations to this large user base. From an academic perspective I have lots of HoloLens …[Read More]
- Dynatrace Managed Instance now available for Azure Government – Built with AI technology, Dynatrace provides full stack, all-in-one monitoring and operations analytics for the public sector at massive scale, in the largest government environments. Dynatrace utilizes artificial intelligence to understand the …[Read More]
- Want to sell Azure but don’t know where to start? – It’s the most trusted, open, and flexible cloud platform. Whether it’s for cloud app development, cloud and infrastructure, data and AI, or security, you’ll be able to give your customers an Azure package that works for them. But with such a broad range of …[Read More]
- Why a developer should build a solution with microservices – Isolated Data & State. As you move ahead you will see how this is done. Meanwhile let’s see what are the advantages you derive out of these. You will analyze the advantages from the perspective of the team involved in the actual development of the code and …[Read More]
- PowerPivot for SharePoint 2016 – Error when trying to schedule a data refresh: “Sorry, something went wrong.” – The SharePoint ULS logs might show the following error: UserProfileDBCache_WCFLogging :: ProfileDBCacheServiceClient.GetUserData threw exception: Access is denied. This can occur when the account running the PowerPivot System Service in SharePoint …[Read More]
- 3 intelligent manufacturing IoT trends to watch out for at HMI 2018 – 1: Enhancing productivity through connected IoT infrastructures Manufacturing technologies—aided by advances in machine learning, artificial intelligence (AI), digitization … secure standards such as OPC UA, data related to real-time manufacturing …[Read More]
Simply stated: Reinforcement Learning deals with actions and rewards (positive or negative). The rewards help to dictate the future actions.
Many children learn via reinforcement learning. Here is a simple example from my childhood. As a child, I was told not to touch the hot stove.
My action: I touched the door of the hot stove when it was open.
My reward (negative): I burned my hand, and it hurt.
My future actions resulted in me staying away from the hot stove.
This was a very simplified example of reinforcement learning. Here are two more great introductory references:
- Simple Beginner’s guide to Reinforcement Learning
- Deep Learning Research Review: Reinforcement Learning
Last week I spent some time chatting with future data scientists. I set up a camera to record some of the answers. Below are a few of the questions addressed.
- How did I transition to data science?
- Why start a data science project?
- Should a new person focus on machine learning or deep learning?
- What is an example data science project?
- Why is real-time important?
Hopefully the videos and answers are helpful to others. Enjoy! And I kept most of the videos fairly short. If you enjoy the videos, please subscribe to the YouTube channel, Learn Data Science. Also, if you have a question you would like answered, please leave a comment below.
Pablo Casas has published a book freely available online, Data Science Live Book. To quote from the book,
It is a book about data preparation, data analysis and machine learning.
The book is open source, and the code examples are written in R.
Columbia University’s course Applied Machine Learning Spring 2018 by Andreas C. Müller has all the lecture notes, slides, homework, and videos posted online.
Andreas is also the author of the book Introduction to Machine Learning with Python.