Machine Learning | Linux Journal

Weekend Reading: Python

Python is easy to use, powerful, versatile and a Linux Journal reader favorite. We've round up some of the most popular recent Python-related articles for your weekend reading. Introducing PyInstaller by Reuven M. Lerner: Want to distribute Python programs to your Python-less clients? PyInstaller is the answer. Bytes, Characters and Python 2 by Reuven M. Lerner: Moving from Python 2 to 3? Here's what you need to know about strings and their role in in your upgrade. Introducing Python 3.7's Dataclasses by Reuven M. Lerner: Python 3.7's dataclasses reduce repetition in your class definitions. Examining Data Using Pandas by Reuven M. Lerner: You don't need to be a data scientist to use Pandas for some basic analysis. Multiprocessing in Python by Reuven M. Lerner: Python's "multiprocessing" module feels like threads, but actually launches processes.

Empowering Linux Developers for the New Wave of Innovation

New businesses with software at their core are being created every day. Developers are the lifeblood of so much of what is being built and of technological innovation, and they are ever more vital to operations across the entire business. So why wouldn't we empower them? Machine learning and IoT in particular offer huge opportunities for developers, especially those facing the crowded markets of other platforms, to engage with a sizeable untapped audience.

ONNX: the Open Neural Network Exchange Format

An open-source battle is being waged for the soul of artificial intelligence. It is being fought by industry titans, universities and communities of machine-learning researchers world-wide. This article chronicles one small skirmish in that fight: a standardized file format for neural networks. At stake is the open exchange of data among a multitude of tools instead of competing monolithic frameworks.

Novelty and Outlier Detection

In my last few articles, I've looked at a number of ways machine learning can help make predictions. The basic idea is that you create a model using existing data and then ask that model to predict an outcome based on new data.

Classifying Text

In my last few articles, I've looked at several ways one can apply machine learning, both supervised and unsupervised. This time, I want to bring your attention to a surprisingly simple—but powerful and widespread—use of machine learning, namely document classification.

Unsupervised Learning

In my last few articles, I've looked into machine learning and how you can build a model that describes the world in some way. All of the examples I looked at were of "supervised learning", meaning that you loaded data that already had been categorized or classified in some way, and then created a model that "learned" the ways the inputs mapped to the outputs.

Testing Models

In my last few articles, I've been dipping into the waters of "machine learning"—a powerful idea that has been moving steadily into the mainstream of computing, and that has the potential to change lives in numerous ways.

Teaching Your Computer

As I have written in my last two articles (Machine Learning Everywhere and Preparing Data for Machine Learning), machine learning is influencing our lives in numerous ways.

Preparing Data for Machine Learning

When I go to Amazon.com, the online store often recommends products I should buy. I know I'm not alone in thinking that these recommendations can be rather spooky—often they're for products I've already bought elsewhere or that I was thinking of buying. How does Amazon do it?

Machine Learning Everywhere

The field of statistics typically has had a bad reputation. It's seen as difficult, boring and even a bit useless. Many of my friends had to take statistics courses in graduate school, so that they could analyze and report on their research. To many of them, the classes were a form of nerdy, boring torture.