91探花

Interested in learning about machine learning, natural language processing, or a second programming language like C? More training from for the winter semester is now open for registration. Sessions are online and free of charge.

If you have questions about upcoming training or ACENET services at 91探花, contact Kaitlin Newson.

February 20 and 22, 2024, 1:00--4:00pm

Apache Spark is a user-friendly open-source platform for large-scale data processing, analytics, and parallel computing. Using Apache Spark and Python (PySpark), this workshop is aimed at analyzing data sets that are too large to be handled and processed by a single computer. With hands-on guided examples, the workshop covers the basics of Spark and Resilient Distributed Datasets (RDD) high-level architecture. The examples are mainly written in Python, hence the APIs covered are the ones available in PySpark, including Spark Core API (RDD API), Spark SQL, and Pandas on Spark. Participants learn how to import data, use functions to transform, reduce, and compile the data, and produce parallel algorithms that can run on Alliance clusters.

Prerequisites: ACENET Basics or equivalent, and how to write functions in Python.

March 5, 2024, 1:00--3:00 pm

How can you make smart decisions about setup and execution of a machine learning project? How should you hire and support the staff working on the project? We hope to provide clear, thoughtful answers to these, and other common questions to get you thinking about whether machine learning is a technology that you and your company or group should think about investing in. We will discuss topics like data collection, the trade-offs involved in choosing a model, and what to expect from a successful project, as well as how to salvage useful by-products and skills when projects don鈥檛 go as planned.

This is a beginner session oriented to business owners and project managers curious to learn more about machine learning, or who may have an idea that involves machine learning and want to know where to begin.

Mar ch13, 2024, 10:00 am--4:00 pm

A great deal of high-performance computing software is written in C, but few universities offer courses in the language any more. If you have to work with "legacy code" written in C, adding features, porting to a new machine, or patching errors, or if you need to write user-defined functions for engineering packages such as Fluent, then this workshop is for you.

Prerequisites: Familiarity with some other programming language.

March 19, 2024, 1:00--3:00pm

Are you curious about machine learning, but not sure where to start, or if the discipline is for you? Join ACENET for a survey and explanation of several methods used to make machines learn. From simple models like Naive Bayes, Regression, and Decision Trees to an introduction to Support Vector Machines and Feed-Forward Neural Networks.

This talk is geared to be approachable to a novice audience, curious about machine learning, but not necessarily math or computer science majors. Methods and techniques will be explained using metaphors, examples, and clear language, without diving too deeply into the math and calculus on which these techniques are based.

March 27, 2024, 10:00 am--4:00 pm

A great deal of high-performance computing software is written in C++, but few universities offer courses in the language any more. If you have to work with "legacy code" written in C++, adding features, porting to a new machine, or patching errors, or if you need to extend packages like OpenFOAM which are written in C++, then this workshop is for you.

C++ was designed as an extension of the C language but has its own distinct idiom or style. This workshop assumes that you already know C to the level reached in the ACENET workshop, "C as a Second Language."

Prerequisites: "C as a Second Language" or prior experience with C programming.

April 2, 2024, 1:00--4:00 pm

Have you wondered how machine learning models can suddenly do so many different types of work? How is it that machines can learn things like language, vision, and translation in such a short amount of time, and what has helped drive these kinds of improvements? The obvious answers--big data and big processors--are only part of the story, and to understand the full picture, we need to take a closer look at the models driving the AI revolution. This talk is aimed at people who are familiar with the basics of feed-forward neural networks, and will involve an in-depth explanation of how information is represented for machines to learn on, how machines can make sense of information, and the challenges presented.

Prerequisite: Familiarity with feed-forward neural networks.

April 16, 2024, 1:00--4:00 pm

How do computers understand language? It seems impossible that zeroes and ones could ever add up to words that humans can understand, but machine language has come a long way in the past few years. Let us take you behind the code to explain how machines simulate language comprehension, and why it鈥檚 a far more complicated problem than 鈥渂onjour = hello.鈥� This talk is aimed at an audience who is not necessarily familiar with computers or language comprehension, but would like a primer to the field, and what it can realistically do. We will explain natural language processing from the perspective of machines that cannot understand words, but capture semantic meaning by processing data.

April 24, 2024, 10:00 am--4:00 pm

Fortran, one of the initial high-level programming languages, continues to be an excellent option for high-performance computing due to its superb performance. The newer versions offer many modern features, including object-oriented programming capabilities to programmers. This course will cover some of these features.

Prerequisites: Familiarity with another programming language.