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Introduction to Deep Learning for Psychology (Deep Learning)
- Dozent/in
- Dr. Alexander Pastukhov
- Angaben
- Seminar
Präsenz/Online parallel 2 SWS
Zeit und Ort: Mi 12:00 - 14:00, M3N/-1.19
- Voraussetzungen / Organisatorisches
- Basic knowledge of Python or R is required. No knowledge of statistics required. Basic school-level algebra knowledge is required.
- Inhalt
- Deep learning is a cutting edge approach to data analysis. The aim of the course aim is to gently introduce key concepts of deep learning, showing the simple tricks that make deep learning so powerful, while highlighting its optimal use and its limitations. We start by creatinga single input-single weight-single output linear neural network and learn how to make it learn to predict the output (a processes of gradual iterative adjustment that is called a "stochastic gradient descent"). We then progress in small steps to multi-input and multi-output networks, to non-linear networks, to multi-layer networks, to advanced (convolutional, recurrent, etc.) networks. Our progress will be slow but steady and you will learn that deep neural networks are both simpler and, yet, more powerful and useful for you than you think.
- Empfohlene Literatur
- "Grokking deep learning" by Andrew W. Trask
https://learning.oreilly.com/library/view/-/9781617293702/?ar
- Englischsprachige Informationen:
- Title:
- Introduction to Deep Learning for Psychology
- Credits: 3
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 12
- Institution: Lehrstuhl für Allgemeine Psychologie und Methodenlehre
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