How to install a Linux desktop on your Android device

Have you ever wished your Android phone or tablet could replace your entire computer? That’s now possible — you can install a Linux or Ubuntu desktop environment on virtually any modern Android device thanks to some clever workarounds. You don’t need to root your phone and you can even use a Bluetooth keyboard and mouse (and maybe an external display) for a powerful desktop-like experience. And even though the experience doesn’t match a real Linux computer, it’s more than usable in a pinch and worth trying out.


To install a Linux environment on your Android device, you can use the Debian NoRoot or UserLAnd apps. If you choose the latter, you’ll also get to choose between various distributions like Kali Linux, Arch, and Ubuntu. While neither app installs a full operating system, they do offer a complete desktop environment and the ability to run many popular Linux programs.


Debian NoRoot: One-click Linux desktop

debian noroot android

Calvin Wankhede / Android Authority

Debian NoRoot offers one of the easiest and least complicated ways to access a Linux desktop on Android. It’s a free app that you need to install via the Play Store. If you haven’t heard of Debian, it’s the flavor of Linux that the popular Ubuntu distribution is based upon. This guarantees compatibility with a wide range of Linux apps and the apt package manager.

Debian NoRoot is pretty lightweight and should run on most Android smartphones and tablets. It’s not the complete Debian operating system — instead, its developer describes it as a “compatibility layer, which allows you to run Debian applications.” How is this possible? Well, Android runs a modified Linux kernel, making it somewhat related to our end-goal. Debian NoRoot also includes all of the basics, including a desktop environment and a terminal application. All in all, it’s a perfect starting point for experienced and novice users alike.

Debian NoRoot lets you access a full-fledged Linux desktop with a simple download from the Play Store.

Once you’ve installed the Debian NoRoot app on your Android device and open it for the first time, it will present you with a list of display resolutions and font scales. Select the default options here, and it will eventually bring you to the desktop.

From this point on, you can immediately get to installing additional Linux programs and apps. We’ll explain how you can do this via the terminal in a later section. For now, consider plugging in a keyboard and mouse since the on-screen touch keyboard can take up a big chunk of your screen’s real estate.

Related: What is a kernel and why does it matter on Android and Linux?

A Novel Algorithm Enables Statistical Analysis of Time Series Data

MIT scientists have developed a novel approach to analyzing time series data sets using a new algorithm, termed state-space multitaper time-frequency analysis (SS-MT). SS-MT gives a structure to dissect time arrangement information progressively, empowering analysts to work in a more educated manner with extensive arrangements of information that are nonstationary, i.e. at the point when their qualities develop after some time.

A Novel Algorithm Enables Statistical Analysis of Time Series Data

Using a novel analytical method they have developed, MIT researchers analyzed raw brain activity data (B). The spectrogram shows decreased noise and increased frequency resolution, or contrast (E and F) compared to standard spectral analysis methods (C and D). Image courtesy of Seong-Eun Kim et al.

It is important to measure time while every task such as tracking brain activity in the operating room, seismic vibrations during an earthquake, or biodiversity in a single ecosystem over a million years. Measuring the recurrence of an event over some stretch of time is a major information investigation errand that yields basic knowledge in numerous logical fields.

This newly developed approach enables analysts to measure the moving properties of information as well as make formal factual correlations between discretionary sections of the information.

Emery Brown, the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience said“The algorithm functions similarly to the way a GPS calculates your route when driving. If you stray away from your predicted route, the GPS triggers the recalculation to incorporate the new information.”

“This allows you to use what you have already computed to get a more accurate estimate of what you’re about to calculate in the next time period. Current approaches to analyses of long, nonstationary time series ignore what you have already calculated in the previous interval leading to an enormous information loss.”  …… 

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AutoML for large scale image classification and object detection

A few months ago, we introduced our AutoML project, an approach that automates the design of machine learning models. While we found that AutoML can design small neural networks that perform on par with neural networks designed by human experts, these results were constrained to small academic datasets like CIFAR-10, and Penn Treebank. We became curious how this method would perform on larger more challenging datasets, such as ImageNet image classification and COCO object detection. Many state-of-the-art machine learning architectures have been invented by humans to tackle these datasets in academic competitions.

In Learning Transferable Architectures for Scalable Image Recognition, we apply AutoML to the ImageNet image classification and COCO object detection dataset — two of the most respected large scale academic datasets in computer vision. These two datasets prove a great challenge for us because they are orders of magnitude larger than CIFAR-10 and Penn Treebank datasets. For instance, naively applying AutoML directly to ImageNet would require many months of training our method.

To be able to apply our method to ImageNet we have altered the AutoML approach to be more tractable to large-scale datasets:

  • We redesigned the search space so that AutoML could find the best layer which can then be stacked many times in a flexible manner to create a final network.
  • We performed architecture search on CIFAR-10 and transferred the best learned architecture to ImageNet image classification and COCO object detection.

With this method, AutoML was able to find the best layers that work well on CIFAR-10 but work well on ImageNet classification and COCO object detection. These two layers are combined to form a novel architecture, which we called “NASNet”.

Our NASNet architecture is composed of two types of layers: Normal Layer (left), and Reduction Layer (right). These two layers are designed by AutoML.