It’s Here. The March 2018 Issue of Linux Journal Is Available for Download Now.

Boasting as many pages as most technical books, this month’s issue of Linux Journal comes in at a hefty 181—that’s 23 articles exploring topics near and dear to everyone from home automation hobbyists to Free Software advocates to hard-core hackers to high-level systems architects.


Weekend Reading: Using Python in Science and Machine Learning

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. more>>

Taking Python to the Next Level

A brief intro to simulating quantum systems with QuTiP. more>>

Introducing Spyder, the Scientific PYthon Development EnviRonment

If you want to use Anaconda for science projects, one of the first things to consider is the spyder package, which is included in the basic Anaconda installation. Spyder is short for Scientific PYthon Development EnviRonment. Think of it as an IDE for scientific programming within Python. more>>

Threading in Python

Threads can provide concurrency, even if they're not truly parallel. more>>

Using Python for Science

Introducing Anaconda, a Python distribution for scientific research.

I've looked at several ways you could use Python to do scientific calculations in the past, but I've never actually covered how to set up and use Python itself in a way that makes scientific work easier. Anaconda does just that. more>>

Visualizing Molecules with Python

Introducing PyMOL, a Python package for studying chemical structures.

I've looked at several open-source packages for computational chemistry in the past, but in this article, I cover a package written in Python called PyMOL. more>>

Learning Data Science

In my last few articles, I've written about data science and machine learning. In case my enthusiasm wasn't obvious from my writing, let me say it plainly: it has been a long time since I last encountered a technology that was so poised to revolutionize the world in which we live. more>>

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. more>>

V. Anton Spraul's Think Like a Programmer, Python Edition

What is programming? Sure, it consists of syntax and the assembly of code, but it is essentially a means to solve problems. To study programming, then, is to study the art of problem solving, and a new book from V. Anton Spraul, Think Like a Programmer, Python Edition, is a guide to sharpening skills in both spheres. more>>

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. more>>

Zed A. Shaw's Learn Python 3 the Hard Way

Author Zed A. Shaw makes a simple promise in his Hard Way series of books from publisher Addison-Wesley Professional: "It'll be hard at first. more>>

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. more>>

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. more>>

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