Training
Wolfram offers optional training workshops in conjunction with our annual Technology Conference so you can learn something new or polish your skill set. Hosted by Wolfram experts, these training sessions can get you up and running more quickly than ever before. Remember, if you're a current Premier Service, Premier Service Plus, Personal License Service or Personal License Service Plus member, you'll save an additional 15% off these already discounted rates.
Training sessions are offered on October 31 at these times: 10am–1pm and 2–5pm Central Time.Wolfram Language Programming Fundamentals
October 31, 10am–1pm Central Time
$100 Standard | $50 Discounted
Get started with the fundamentals. Topics covered include lists and associations; assignments, rules and patterns; syntax and expressions; pure functions and useful tips for best practice.
Practical Programming with Wolfram Language
October 31, 2–5pm Central Time
$100 Standard | $50 Discounted
Take the next step in building your programming skills with a deeper exploration of patterns, good coding practices, the Manipulate super function, custom interfaces and cloud deployment.
The Wolfram Plugin for ChatGPT
October 31, 10am–1pm Central Time
$100 Standard | $50 Discounted
Learn how the Wolfram plugin makes ChatGPT smarter by giving it access to powerful computation, accurate math, curated knowledge, real-time data and visualization through Wolfram|Alpha and Wolfram Language. This class showcases many different examples of computational queries and covers various technical aspects including techniques for setting up prompts to enhance a human-AI workflow.
Wolfram Tools for AI
October 31, 2–5pm Central Time
$100 Standard | $50 Discounted
Learn about tools available in Wolfram Language to work with AI systems using both natural language and high-level computational language. This course will start out with a basic introduction to automated and highly customizable machine learning functions in Wolfram Language. It will discuss resources like the symbolic neural network framework, the neural net repository and also provide examples of using the newly introduced LLM functionality.