MathsDL-Spring18

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MathsDL-spring18 is an Open Source research and education tool that provides course materials for the Mathematics of Deep Learning. It includes lecture slides, references, and detailed syllabi for a graduate-level topics course.

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Yes

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Technical

About

What is MathsDL-spring18?

MathsDL-spring18 is an open-source repository offering comprehensive materials for the 'Mathematics of Deep Learning' topics course, taught at NYU in Spring 2018. It provides detailed logistics, instructor information, and a full syllabus covering geometric aspects of deep learning, optimization, and generalization. The repository includes lecture slides, references, and outlines for parallel curricula focusing on topics like Dynamic Programming, Policy Learning, and Monte-Carlo Tree Search, with specific readings and questions for each session. This resource is invaluable for students and researchers interested in the theoretical and mathematical foundations of deep learning, offering a structured approach to complex concepts and open problems in the field.

Best used for

Ideal for professors and teachers who need to access structured academic content for deep learning courses, understand the mathematical underpinnings of AI, and explore advanced topics like geometric stability and optimization. Especially valuable for those seeking detailed syllabi, lecture slides, and curated reading lists for graduate-level study.

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access course materials

learn deep learning math

study deep learning theory

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Capabilities

Key features

Lecture slides
Course syllabus
Reading references
Lab session materials
Parallel curricula

Target Audience

professorteacher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What topics are covered in the MathsDL-spring18 course materials?

The course materials cover the Mathematics of Deep Learning, focusing on geometrical aspects, optimization, generalization, and learning. Specific topics include Euclidean and Non-Euclidean Geometry, Stochastic Optimization, Information Geometry, and the landscape of Deep Learning Optimization, alongside parallel curricula on Dynamic Programming and Monte-Carlo Tree Search.

Who are the instructors for the MathsDL-spring18 course?

The lecture instructor for the MathsDL-spring18 course was Joan Bruna (bruna@cims.nyu.edu). The tutor for the optional Parallel Curricula was Cinjon Resnick (cinjon@nyu.edu). Contact information is provided for inquiries related to the course content.

What are the prerequisites for understanding the MathsDL-spring18 course content?

Solid undergraduate-level knowledge of Multivariate Calculus, Linear Algebra, Probability, and Statistics is required. Notions of Harmonic Analysis, Differential Geometry, and Stochastic Calculus are beneficial but not strictly essential for engaging with the course materials.

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