ACCESS HPC Workshop Series
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Monthly workshops sponsored by ACCESS on a variety of HPC topics organized by Pittsburgh Supercomputing Center (PSC). Each workshop will be telecast to multiple satellite sites and workshop materials are archived.
Attention, Transformers, and LLMs: a hands-on introduction in Pytorch
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This workshop focuses on developing an understanding of the fundamentals of attention and the transformer architecture so that you can understand how LLMs work and use them in your own projects.
Introduction to Deep Learning in Pytorch
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This workshop series introduces the essential concepts in deep learning and walks through the common steps in a deep learning workflow from data loading and preprocessing to training and model evaluation. Throughout the sessions, students participate in writing and executing simple deep learning programs using Pytorch – a popular Python library for developing, training, and deploying deep learning models.
Solving differential equations with Physics-informed Neural Network
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Differential equations, the backbone of countless physical phenomena, have traditionally been solved using numerical methods or analytical techniques. However, the advent of deep learning introduces an intriguing alternative: Physics-Informed Neural Networks (PINNs). By leveraging the representational power of neural networks and integrating physical laws (like differential equations), PINNs offer a novel approach to solving complex problems. This guide walks through an implementation of a PINN to solve DEs such as the logistic equation.
What are LSTMs?
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This reading will explain what a long short-term memory neural network is. LSTMs are a type of neural networks that rely on both past and present data to make decisions about future data. It relies on loops back to previous data to make such decisions. This makes LSTMs very good for predicting time-dependent behavior.
Framework to help in scaling Machine Learning/Deep Learning/AI/NLP Models to Web Application level
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This framework will help in scaling Machine Learning/Deep Learning/Artificial Intelligence/Natural Language Processing Models to Web Application level almost without any time.
Awesome Jupyter Widgets (for building interactive scientific workflows or science gateway tools)
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A curated list of awesome Jupyter widget packages and projects for building interactive visualizations for Python code
Automated Machine Learning Book
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The authoritative book on automated machine learning, which allows practitioners without ML expertise to develop and deploy state-of-the-art machine learning approaches. Describes the background of techniques used in detail, along with tools that are available for free.
Representation Learning in Deep Learning
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Representation learning is a fundamental concept in machine learning and artificial intelligence, particularly in the field of deep learning. At its core, representation learning involves the process of transforming raw data into a form that is more suitable for a specific task or learning objective. This transformation aims to extract meaningful and informative features or representations from the data, which can then be used for various tasks like classification, clustering, regression, and more.
AI/ML TechLab - Accelerating AI/ML Workflows on a Composable Cyberinfrastructure
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This technology lab contains a set of sessions to help a new user start an AI project on the ACES cluster, a composable accelerator testbed at Texas A&M University. You will learn how to create and activate a virtual environment, manipulate and visualize data with Pandas and Matplotlib, use Scikit-learn for linear regression and classification applications, and use Pytorch to create and train a simple image classification model with deep neural networks (DNN).
Active inference textbook
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This textbook is the first comprehensive treatment of active inference, an integrative perspective on brain, cognition, and behavior used across multiple disciplines including computational neurosciences, machine learning, artificial intelligence, and robotics. It was published in 2022 and it's open access at this time. The contents in this textbook should be educational to those who want to understand how the free energy principle is applied to the normative behavior of living organisms and who want to widen their knowledge of sequential decision making under uncertainty.