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Foundations of Mathematics for Deep Learning




This intensive block course is designed to bridge the gap between basic high-school mathematics and the advanced mathematical concepts essential for deep learning. It equips students with the foundational knowledge and skills required to excel in deep learning applications, including Natural Language Processing and Computer Vision. The course is tailored for students aiming to prepare for deep-learning-based courses at the institute, offering a comprehensive journey from fundamental mathematical principles to their practical application in deep learning models.

Course Content:
- Linear Algebra and Its Application to Data: The course begins with an exploration of linear algebra, focusing on its significance in understanding and manipulating data. Students will learn about vectors and matrices, mastering the art of working with these structures to perform various data operations.

- Multivariate Calculus for Data Fitting: Building upon the linear algebra foundation, the course delves into multivariate calculus to demonstrate how to optimize fitting functions for precise data modeling. Starting from introductory calculus concepts, the course employs matrices and vectors to facilitate a deep understanding of data fitting techniques.

- Operationalizing Concepts with Python: Transitioning from theory to practice, this segment emphasizes the application of learned concepts through Python programming. Students will engage in numerous exercises to solidify their understanding and gain hands-on experience in mathematical modeling.

- Implementing Neural Networks: The culmination of the course focuses on the construction and training of neural networks. By applying linear algebra and calculus, students will comprehend the mechanics of forward and backward passes within neural networks. The course includes a practical project where participants will implement a simple neural network and the backpropagation algorithm from scratch, applying it to a learning task.

- Coding Interview-Inspired Exercises for Research Scientists and Engineers: In addition to the core curriculum, this course includes exercises inspired by coding interviews for research scientists and research engineers in big tech companies. These exercises are designed to challenge students and prepare them for the types of problem-solving scenarios they might encounter in a professional setting, further enhancing their readiness for careers in deep learning research and engineering.

- A basic understanding of high-school mathematics.
- Familiarity with Python or another programming language, although all course materials and projects will utilize Python.

Upon completion, participants will not only grasp the mathematical theories underpinning deep learning but also gain practical skills in applying these concepts through programming. Additionally, they will be equipped with the problem-solving abilities needed to excel in technical interviews and research roles within the tech industry. This course ensures students are well-prepared and confident to tackle the challenges of deep-learning-based courses and projects.

Weitere Angaben

Ort: nicht angegeben
Zeiten: Termine am Montag, 16.09.2024 12:00 - 18:00, Dienstag, 17.09.2024 09:00 - 12:00, Dienstag, 17.09.2024 13:00 - 16:00, Mittwoch, 18.09.2024 09:00 - 12:00, Mittwoch, 18.09.2024 13:00 - 16:00, Donnerstag, 19.09.2024 09:00 - 12:00, Donnerstag, 19.09.2024 13:00 - 16:00, Freitag, 20.09.2024 09:00 - 12:00, Freitag, 20.09.2024 13:00 - 16:00, Montag, 23.09.2024 12:00 - 18:00, Dienstag, 24.09.2024 09:00 - 12:00, Dienstag, 24.09.2024 13:00 - 16:00, Mittwoch, 25.09.2024 09:00 - 12:00, Mittwoch, 25.09.2024 13:00 - 16:00, Donnerstag, 26.09.2024 09:00 - 12:00, Donnerstag, 26.09.2024 13:00 - 16:00, Freitag, 27.09.2024 09:00 - 12:00, Freitag, 27.09.2024 13:00 - 16:00
Erster Termin: Montag, 16.09.2024 12:00 - 18:00
Veranstaltungsart: Blockseminar (Offizielle Lehrveranstaltungen)


  • Cognitive Science > Bachelor-Programm
  • Cognitive Science > Master-Programm
  • Human Sciences (e.g. Cognitive Science, Psychology)