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SUMMARY:Jakub Malinowski (Dioscuri Centre in Topological Data Analysis)
DTSTART:20260309T113000Z
DTEND:20260309T133000Z
DTSTAMP:20260423T024551Z
UID:BNAT/17
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/BNAT/17/">De
 ep Learning (Part 1)</a>\nby Jakub Malinowski (Dioscuri Centre in Topologi
 cal Data Analysis) as part of Basic Notions and Applied Topology Seminar\n
 \nLecture held in Room 1 at the IMPAS\, Room 1.14 at the Institute of Info
 rmatics (University of Gdańsk).\n\nAbstract\nThis first session introduce
 s the fundamental concepts and motivations behind deep learning. We begin 
 with a discussion of why and when deep learning can outperform traditional
  statistical methods - especially for large\, high-dimensional data. Next\
 , we explore the architecture of neural networks: from simple single-layer
  networks to multilayer (deep) networks. Key learning mechanisms - includi
 ng backpropagation\, regularization\, and stochastic gradient descent (SGD
 ) - will be explained intuitively and with math as appropriate. We will al
 so review practical considerations (e.g.\, network tuning\, overfitting\, 
 capacity control)\, providing Python code examples to illustrate how deep 
 networks are defined and trained in a real-world context.\n
LOCATION:https://researchseminars.org/talk/BNAT/17/
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