On-line Deep Learning Workshop August 24!machine-learning, python SWEETER Project Portal
The NVIDIA Deep Learning Institute (DLI), the Hewlett Packard Enterprise Data Science Institute at the University of Houston, the Texas A&M Institute of Data Science, the Texas A&M High PeRead More →
Improving alphafold performance and applications of alphafold
The project involves the improvements of the alphafold program. The projects includes two parts, improvements of the program and possible applications of it in collaboration with different researchers. The effort will be in coordination of research groups interested in the application of alphafold.
Specifically the performance improvements involves a hierarchical/diverse aspects of the code, (1)I/O, (2) databases query, (3) multithread/multi process in python, as well as GPU acceleration, and more.
High Performance Computing vs Quantum Computing for Neural Networks supporting Artificial Intelligence
A personalized learning system that adapts to learners' interests, needs, prior knowledge, and available resources is possible with artificial intelligence (AI) that utilizes natural language processing in neural networks. These deep learning neural networks can run on high performance computers (HPC) or on quantum computers (QC). Both HPC and QC are emergent technologies. Understanding both systems well enough to select which is more effective for a deep learning AI program, and show that understanding through example, is the ultimate goal of this project. The entry to learning technologies such as HPC and QC is narrow at present because it relies on classical education methods and mentoring. The gap between the knowledge workers needed, which is in high demand, and those with the expertise to teach, which is being achieved at a much slower rate, is widening. Here, an AI cognitive agent, trained via deep learning neural networks, can help in emergent technology subjects by assisting the instructor-learner pair with adaptive wisdom. We are building the foundations for this AI cognitive agent in this project.
The role of the student facilitator will involve optimizing a deep learning neural network, comparing and contrasting with the newest technologies, such as a quantum computer (and/or a quantum computer simulator) and a high performance computer and showing the efficiency of the different computing approaches. The student facilitator will perform these tasks at the rate described in the proposal. Milestone work will be displayed and shared publicly via posting to the Jupyter Notebooks on Google Colab and linked to regular Github uploads.
|AHPCC documentary||Documentation||login, batch-jobs, slurm, bash, ssh, python, mpi||Beginner, Intermediate|
|Applications of Machine Learning in Engineering and Parameter Tuning Tutorial||Learning||data-analysis, machine-learning, python||Beginner, Intermediate|
|Automated Machine Learning Book||Learning||ai, data-analysis, deep-learning, machine-learning, neural-networks, python, r||Intermediate, Advanced, Expert|
|AI Institutes Cyberinfrastructure||Gathering place for AI researchers to find curated information about using ACCESS resources for AI applications and research.||ai, machine-learning, gpu, python|