This page requires that JavaScript be enabled in your browser.
Learn how »
Neural Network Research with Wolfram Language
Ian Wright
Wolfram Language provides an advanced framework for rapidly prototyping neural network applications and exploring research ideas. In this talk, I demonstrate the language features that helped me develop a novel neural network that learns compact non-differentiable Boolean functions.
Thanks for your feedback.
Channels: Technology Conference
1311 videos match your search.
|
Eric Mjolsness Collaborative projects have resulted in several Mathematica-implemented modeling languages aimed at general-purpose biological modeling, which is a useful and topical but an indefinitely expandable goal. We update previous work on ... |
|
Jae Bum Jung/Yan Zhuang |
|
Phillip Todd |
|
Василий Сороко |
|
Phil Ramsden |
|
Lou D'Andria Constructing interfaces with Dynamic, DynamicModule and Manipulate is nothing new, but those aren't the only Dynamic primitives available in Mathematica. In this talk, we'll identify and demonstrate some of the ... |
|
Галина Михалкина, Григорий Фридман |
|
Галина Михалкина |
|
Андрей Кротких |
|
Антон Екименко, Кирилл Белов |
|
Физический институт имени П.Н. Лебедева |
|
Григорий Фридман, Олег Иванов |
|
Галина Михалкина |
|
Олег Кофнов |
|
Николай Сосновский |
|
Микаэл Эгибян |
|
Микаэл Эгибян |
|
Леонид Шифрин |
|
Вахагн Геворгян |
|
Алексей Семенов |