Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving. Python has gained widespread popularity as a computing language: It is nowadays employed for computing by practitioners in such diverse fields as for example scientific research, engineering, finance, and data analytics. One reason for the popularity of Python is its high-level and easy-to-work-with syntax, which enables the rapid development and exploratory computing that is required in modern computational work. After reading and using this book, you will have seen examples and case studies from many areas of computing, and gained familiarity with basic computing techniques such as array-based and symbolic computing, all-around practical skills such as visualisation and numerical file I/O, general computational methods such as equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling and machine learning. Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical capabilities in Python, its standard library, and the extensive ecosystem of computationally oriented Python libraries, including popular packages such as NumPy, SciPy, SymPy, Matplotlib, Pandas, and more, and how to apply these software tools in computational problem solving.

This post was contributed by Zilliant Pricing Scientist Amir Meimand, who will be presenting at INFORMS in Nashville, Tennessee. In this post, Amir gives a primer on pricing science and explains how machine learning empowers organizations to respond swiftly with price after a change in market dynamics. Let's Start at the Beginning: A Primer on Pricing Science Pricing science started in the early 1980s with the airline industry. These models were mostly based on descriptive and predictive analytics to set price according to forecasted demand and available number of seats. Since about 2000, the application of pricing science has taken off in B2B.

The current situation with malware naming conventions is in disarray. Different antivirus vendors use different naming conventions and sometimes they don't follow their own standards. Let's look at a few results for a random virus. These are the results from VirusTotal, a meta-antivirus scanning service. We can see that it is a Trojan malware with some vendors (Dr.Web and TrendMicro) setting the family as StartPage, some saying it's in the Agent family, some saying it is in the FakeAV family and some saying it is Generic "KR" malware.

Artificial intelligence is taking over the world whether we want it or not, so we can either make it work for us or against us. But how? That's one of the things we talked about with some fellow startuppers last week. It's good to experience the startup community firsthand from time to time. It helps you keep your finger on the pulse much more effectively than just gathering data online or talking to people on Facebook and Twitter. That's exactly why we decided to go to the SaaS Meetup, to see what the perceived trends in the startup world are, and to talk to some founders to find out whether these trends actually mean something.

With the release of Google Allo last week, we've officially entered the era of the assistant. Every company that owns a major tech platform is now betting that assistants will be an important interface in the post-mobile world. You might think assistants today are trivial or stupid, and you wouldn't be wrong. They often don't understand what we say and can't hold real conversations yet. Mostly they're relegated to simple tasks like playing music, sending texts, or setting timers.

Yahoo is the latest tech company to open source its computer vision code. Yahoo hopes that its convolutional neural net (CNN) will empower others to better guard innocent eyes, but admits that because of the tech's very nature (and how the definition of "porn" can vary wildly), that the CNN isn't perfect. "This model is a general purpose reference model, which can be used for the preliminary filtering of pornographic images," a post on the Yahoo Engineering Tumblr says. "We do not provide guarantees of accuracy of output, rather, we make this available for developers to explore and enhance as an open source project." The code is available on Github at the moment, and if you need any testing material, well, there isn't exactly a shortage of it on Tumblr.

Decision trees are used extensively in machine learning because they are easy to use, easy to interpret, and easy to operationalize. KD Nuggets, one of the most respected sites for data science and machine learning, recently published an article that identified decision trees as a "top 10" algorithm for machine learning. If you are new to machine learning, some of these concepts may be unfamiliar. The goal of this blog is to provide you with the basics of decision trees using Talend and Apache Spark. If you want to learn more about advanced analytics, please see the references section below.(2)

The University of Pennsylvania, the largest private employer in Philadelphia, is a world-renowned leader in education, research, and innovation. This historic, Ivy League school consistently ranks among the top 10 universities in the annual U.S. News & World Report survey. Penn has 12 highly-regarded schools that provide opportunities for undergraduate, graduate and continuing education, all influenced by Penn's distinctive interdisciplinary approach to scholarship and learning. Penn offers a unique working environment within the city of Philadelphia. The University is situated on a beautiful urban campus, with easy access to a range of educational, cultural, and recreational activities.

Machine Learning is rapidly becoming a required and critical component of engineering organizations across the tech industry. From movie recommendation algorithms to self-driving cars, it is clearly an exciting and compelling field. Companies are hiring armies of Machine Learning researchers to solve difficult problems like voice and object recognition. What does this all mean to the average software engineer? In many cases, extremely specialized knowledge is necessary to outperform existing state-of-the-art systems.

Greg Linden may not be a household name, but he changed the way we interact with culture and transformed retail forever. An engineer at Amazon in the late 1990s, Linden worked on a curious problem: how to recommend books without human intervention. Until then Amazon relied on editors who wrote hundreds of reviews every year. It was a costly and time-consuming process. Automating recommendations proved trickier than anyone expected.