Linear regression is likely the first algorithm that you would learn when starting down a career path  in data science or AI, because it’s simple to implement and easy to apply in real-time.

Here’s a great primer on how to do linear regression in TensorFlow 2.0.

This algorithm is widely used in data science and statistical fields to model the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). Several types of regression techniques are available based on the data being used. Although linear regression involves simple mathematical logic, its applications are put into use across different fields in real-time. In this article, we’ll discuss linear regression in brief, along with its applications, and implement it using TensorFlow 2.0.

Applied Science explores a type of optics that defy conventional logic.

Telecentric and hypercentric optics are very different from our eyes or normal camera lenses. They have “negative” perspective or no perspective, respectively, leading to very unusual images. In this video I show how to use a common fresnel lens in the creation of your own telecentric optical system.

Looking for a good DIY (Do It Yourself) IoT project?

There are so many interesting things that we can do with a few components and a little creatively.

In this video, Cam Soper comes on to show us how he automated his home with .NET Core, Hubitat, and Azure with his open source project called Puppet.

Time Index:

  • [01:00] – Taking a look at the setup
  • [02:29] – Triggering Hubitat events
  • [05:15] – Using .NET for interaction
  • [08:52] – The motivation behind Puppet
  • [13:35] – Running your own Puppet server

Useful Links

    Commercially viable quantum computing could be here sooner than you think, thanks to a new innovation that shrinks quantum tech down onto a chip: a cryochip.

    Seeker explains:

    It seems like quantum computers will likely be a big part of our computing future—but getting them to do anything super useful has been famously difficult. Lots of new technologies are aiming to get commercially viable quantum computing here just a little bit faster, including one innovation that shrinks quantum technology down onto a chip.

    Usually, super-computers installed at academic and national labs get configured once, bought as quickly as possible before the grant money runs out, installed and tested,  and put to use for a four or five years or so.

    Rarely is a machine upgraded even once, much less a few times.

    But that is not he case with the “Corona” system at Lawrence Livermore National Laboratory, which was commissioned in 2017 when North America had a total solar eclipse – and hence its name.

    While this machine, procured under the Commodity Technology Systems (CTS-1) to not only do useful work, but to assess the CPU and GPU architectures provided by AMD, was not named after the coronavirus pandemic that is now spreading around the Earth, the machine is being upgraded one more time to be put into service as a weapon against the SARS-CoV-2 virus which caused the COVID-19 illness that has infected at least 2.75 million people (confirmed by test, with the number very likely being higher) and killed at least 193,000 people worldwide.

    The impact of Covid-19 is being felt everywhere across the globe.

    To remain safe from coronavirus, most people have quarantined themselves at home. Others, who aren’t able to work from home and haven’t been labelled as essential workers, are understandably concerned about their future.

    I’m of the opinion that it’s always a good time to learn, but I feel that now is a better time than ever.

    For those who are uncertain about their future, there has never been a better time to enrol in e-learning courses. A rigorous e-learning course teaches skills employers want. With ample free time on their hands and faced with an uncertain future, professionals must take steps to increase their employability. In an economy where skills acquired just a few years ago quickly become obsolete, professionals must enrol in new courses to keep their talent set relevant.

    Alex Bocharov, Principal Researcher at Microsoft Quantum Systems group and Chris Granade, Senior Research Software Development Engineer join Vadim Karpusenko to discuss the impact of Quantum Computing on the Machine Learning and Artificial Intelligence domains.

    Touching briefly on decade-old pioneering results in Quantum Machine Learning, the story switches to describe more recent technologies meant for near term generation of smaller “noisy” quantum computers. The second part of the interview showcases how you can get started using quantum machine learning with Q# and the QML library provided with the Microsoft Quantum Development Kit.

    Time Index:

    • [00:10] – Introducing Alex Bocharov : intro into Quantum Computing
    • [02:16] – What is the time horizons for Quantum Neural Networks in practice?
    • [02:52] – Desirable properties of the variational quantum circuits
    • [06:00] – Introducing Chris Granade: How to use QC for ML/AI?
    • [06:50] – What does quantum development look like?
    • [08:45] – Do you need to learn Quantum gates to use Quantum Computing?
    • [11:05] – Executing Quantum code in Python Jupyter Notebook
    • [13:00] – Download is available at aka.ms/QML

    More Information: