Software


Learn how to become a computer programmer by taking some online courses

Mashable

Heads up: All products featured here are selected by Mashable's commerce team and meet our rigorous standards for awesomeness. If you buy something, Mashable may earn an affiliate commission. These days, it seems like everyone is learning to code. But it's not as easy as it seems to get started – t...



Honeywell rolls out two rugged computers to streamline fulfillment – DC Velocity

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Handhelds connect to warehouse software platforms, firm says. Honeywell International Inc. has rolled out two rugged mobile computers that it said will streamline fulfillment operations by connecting workers and DCs to cloud-based databases and the Internet of Things (IoT). The Dolphin CN80 Mobile...


AI Programming: 5 Most Popular AI Programming Languages - DZone AI

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Python is among developers' favorite programming languages for AI development because of its syntax, simplicity, and versatility. Python is very encouraging for machine learning for developers as it is less than languages such as C and Java. It also a very portable language as it is used on platfo...


Top 25 Python Programming Books

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Get a comprehensive, in-depth introduction to the core Python language with this hands-on book. Based on author Mark Lutz's popular training course, this updated fifth edition will help you quickly write efficient, high-quality code with Python. It's an ideal way to begin, whether you're new to prog...


Which Is the Best Programming Language for AI Machines?

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Artificial Intelligence is in limelight these days! While AI machines are finding their application in almost every field, but to program them is still an uphill task. As there is no programming language yet that fulfills the demands and can be used only to program the AI machines, other languages a...


One million Linux and open-source software classes taken

ZDNet

We all know how popular and helpful Linux and open source products are, but since most of them are available for free, how do the companies that produce them make any money to pay their bills? As it turns out, lots of ways. Want to get a job in IT? Then, you need to know Linux and open-source softw...


Open Source turns 20: Here's how it all started

Engadget

In the dead of winter 20 years ago, Netscape -- inspired in part by a treatise on Linux and free software development -- released the source code for its Netscape Communicator web browser. This was a publicly traded company that had just reported some disappointing financials announcing to the world that it would make the core of its product available to thinkers, tinkerers and the insatiably curious. Over the days that followed, a cadre of software developers and advocates agonized over a crucial question: What should this kind of stuff be called? After some prolonged discussions and a few phone calls with Netscape, they had their answer. And thus, 20 years ago, the term "Open Source" was born.


Installing TensorFlow for Java TensorFlow

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TensorFlow provides APIs for use in Java programs. These APIs are particularly well-suited to loading models created in Python and executing them within a Java application. This guide explains how to install TensorFlow for Java and use it in a Java application. This guide explains how to install TensorFlow for Java. The installation instructions for Android are in a separate Android TensorFlow Support page.


Introducing Seldon Core -- Machine Learning Deployment for Kubernetes

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Seldon Core focuses on solving the last step in any machine learning project to help companies put models into production, to solve real-world problems and maximise the return on investment. Data scientists are freed to focus on creating better models while devops teams are able to manage deployments more effectively using tools they understand. Instead of just serving up single models behind an API endpoint, Seldon Core allows complex runtime inference graphs to be deployed in containers as microservices. Efficiency -- traditional infrastructure stacks and devops processes don't translate well to machine learning, and there is limited open-source innovation in this space, which forces companies to build their own at great expense or to use a proprietary service. Also, data engineers with the necessary multidisciplinary skillset spanning ML and ops are very scarce.