![]() ![]() For general information about NAS-provided software modules, see Software Directories and Using Software Modules. ![]() The conda environments are provided via software modules in HECC system directories. To learn more, see Using Conda Environments for Machine Learning. Also, the environments may contain additional packages to the ones listed here. The environments contain many of the same software programs, but the package versions may be different. The following table shows currently available conda environments (all include GPU support): Environment Name We provide multiple conda environments that include basic machine learning packages, as well as common image processing and natural language processing packages, for your machine learning projects. If you are a new user, be sure to read through our New User Orientation and complete the first-time login process. Once you have a NAS account, you will have access to software and infrastructure for your machine learning project. Please check back often, as new articles will become available soon. In this section of the Knowledge Base, we provide information that can help you get started with a machine learning environment and short tutorials describing how to use the machine learning tools available on NAS systems. Predicting Composition of Photo Voltaic Cells Using Neural Networks (PDF)įor more information about working with our team, please contact us at Getting Started with Machine Learning.Carbon Nanotube Gas Sensor Using Neural Networks (PDF).The Data Science Team is currently working with NASA researchers on a number of projects, including: For general information, see HECC Data Science Services. HECC offers a range of services for researchers moving into advanced data science, using the latest technologies in statistics-based data analytics, machine learning, and deep learning. Once the data is organized, it can then be analyzed using powerful statistical methods. These methods organize data in a way that is both systematic (collected, processed, and stored methodically according to a standard practice) and semantic (unambiguous and logical, in both order and representation, so that relationships and biases can be easily explored). Machine learning, deep learning, and artificial intelligence have become essential tools for handling and gaining insight from the enormous amounts of data that are being generated via high-performance computing, modern modeling and simulation, and instrument technology. ![]()
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