I can safely predict that if you are a developer, you are looking for ways to get your job done faster. Get working code quicker, find bugs faster, take advantage of new technologies and get working performance as smoothly as possible. This is why the saying "steal with pride" comes up --it's a well-worn technique to borrow or adopt code where you can. (Of course, I'm not saying you would commit...
April showers bring you Python* code samples, a recap of GDC, machine learning in retail and an update to Intel® Threading Building Blocks. Read on!
Check out the new commercial IoT website, find out more about Embree ray tracing, and read about a long-lived game studio.
Intel® Developers and Innovators were busy over the last month! Here’s an update on what the Intel® Software Innovators, Intel® Black Belt Software Developers, and Intel® Student Ambassadors were up to around the globe.
This Color Match Game application is part of a series of how-to Internet of Things (IoT) code sample exercises using the Intel® IoT Developer Kit, and a compatible Intel® IoT Platform cloud platforms, APIs, and other technologies.
This article describes how to install and run Unity Technologies ML-Agents* in CPU-only environments.
本文描述了如何在仅限 CPU 的环境中安装并运行 Unity Technologies ML-Agents*。本文展示了如何：在 Windows* 上训练并运行 ML-Agents，执行 TensorFlow* CMake 构建，以及从零创建一个简单的 Amazon Web Services Ubuntu* Amazon Machine Image* 环境。
This article will show game developers how to use reinforcement learning to create better artificial intelligence (AI) behavior. Using Intel® Distribution for Python—an improved version of the popular object-oriented, high-level programming language—readers will glean how to train pre-existing machine-language (ML) agents to learn and adapt. In this scenario, we will use Intel® Optimization for...
In the final part of this two-part series on machine learning with Unity* ML-Agents, we will dig deeper into the architecture and create an ML-Agent from scratch. Before training, we will inspect the files that require parameters for machine learning to proceed. Finally, we will train the agent using Intel® optimized Python* and show how the completed system works.