Since 2015, the non-profit organization, Chicas en Tecnologa, has been developing a range of programs to bridge the technological gender gap in Argentina, by encouraging, fostering and increasing the …

With the growing buzz around data science, many professionals want to learn how to become a data scientistthe role Harvard Business Review called the "sexiest job of the 21st century." Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.

Thomas Endres and Samuel Hopstock demonstrate how to apply machine learning techniques on a program's source code, covering problems you may encounter, how to get enough relevant training data, how to encode the source code as a feature vector so that it can be processed mathematically, what machine learning algorithms to use, and more.

With the growing buzz around data science, many professionals want to learn how to become a data scientistthe role Harvard Business Review called the "sexiest job of the 21st century." Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.

The days of deploying Java code to Hadoop and Spark data lakes for data science and ML are numbered. Welcome to the future. Containers and Kubernetes make great language-agnostic distributed computing clusters: it's just as easy to deploy Python as it is Java. Mathew Lodge shows you how.

The days of deploying Java code to Hadoop and Spark data lakes for data science and ML are numbered. Welcome to the future. Containers and Kubernetes make great language-agnostic distributed computing clusters: it's just as easy to deploy Python as it is Java. Mathew Lodge shows you how.

The honeymoon era of data science is ending, and accountability is coming. Successful data science leaders deliver measurable impact on an increasing share of an enterprises KPIs. Joshua Poduska and Patrick Harrison detail how leading organizations have taken a holistic approach to people, process, and technology to build a sustainable competitive advantage

In an effort to broaden graduates' mathematical toolkit and address gender equity in STEM education, Rob Newton has led the implementation of Python projects across his school's entire ninth-grade math courses. Now every student in the ninth grade completes three python projects that introduce programming and integrate them with the ideas developed in class.

Since Bitcoin was open sourced in 2009, we've been reading about how cryptocurrencies are the new internet. But how do they actually work? Lucy Wyman offers a deep dive into blockchains, covering what a blockchain is, how it works, the cool math and theory that it uses, and applications beyond cryptocurrencies.

Do you want to create robust and composable abstractions? Eric Normand shares an iterative process to define the essence of a domain and build composability into the core and then demonstrates how to apply this process to the Processing graphics library to develop a composable vector graphics system.

Do you have a real understanding of the performance of your new Kubernetes service, or do you just know what the average user is seeing? Fred Moyer explains how to get a comprehensive understanding of your Kubernetes infrastructure with a little math and an Istio service mesh implementation for your container-based infrastructure.

Functional programming is all the rage these days, but with lingo like "lambdas," "functors," and "monads," it can be intimidating to get started. Join Kyle Shevlin to learn just enough functional programming for you and your team to get started without getting lost in the vernacular.

Mathew Salvaris, Miguel Gonzalez-Fierro, and Ilia Karmanov offer a comparison of two platforms for running distributed deep learning training in the cloud, using a ResNet network trained on the ImageNet dataset as an example. You'll examine the performance of each as the number of nodes scales and learn some tips and tricks as well as some pitfalls to watch out for.

Advancements in computing technologies and ecommerce platforms have amplified the risk of online fraud, which results in billions of dollars of loss for the financial industry. This trend has urged companies to consider AI techniques, including deep learning, for fraud detection. Francesca Lazzeri and Jaya Mathew explain how to operationalize deep learning models with Azure ML to prevent fraud.

How can you most effectively use machine intelligence to drive strategy? By merging it in the right way with the human ingenuity of leaders throughout your organization. Stephanie Beben shares insights from her work with pioneering companies, government agencies, and nonprofits that are successfully navigating this partnership by becoming mathematical corporations.