Search results for: MOLECULAR MODELING,MOLECULES,NEURAL NETWORKS,SOLVENTS,VISCOSITY

• Deep neural networks for data analysis 24/25

  e-Learning Courses

  • J. Cychnerski
  • K. Draszawka

  This course covers introduction to supervised machine learning, construction of basic artificial deep neural networks (DNNs) and basic training algorithms, as well as the overview of popular DNNs architectures (convolutional networks, recurrent networks, transformers). The course introduces students to popular regularization techniques for deep models. Besides theory, large part of the course is the project in which students apply...

• Deep neural networks for data analysis

  e-Learning Courses

  • K. Draszawka

  The aim of the course is to familiarize students with the methods of deep learning for advanced data analysis. Typical areas of application of these types of methods include: image classification, speech recognition and natural language understanding. Celem przedmiotu jest zapoznanie studentów z metodami głębokiego uczenia maszynowego na potrzeby zaawansowanej analizy danych. Do typowych obszarów zastosowań tego typu metod należą:...

• Modeling the Networks - ed. 2021/2022

  e-Learning Courses

  The goal of this course is to present optimization problems for road networks, where the road network is a set of n distinct lines, or n distinct (open or closed) line segments, in the plane, such that their union is a connected region.

• Deep neural networks for data analysis 27/28

  e-Learning Courses

  • K. Draszawka

• Deep neural networks for data analysis 25/26

  e-Learning Courses

  • K. Draszawka

• Deep neural networks for data analysis 26/27

  e-Learning Courses

  • K. Draszawka

• Materials science. Quantum particle approach. 2022.

  e-Learning Courses

  • M. Bobrowski

  quantum methods for materials and molecular modeling.

• Material Science Quantum Particle Approach 2021

  e-Learning Courses

  • M. Bobrowski

  quantum methods for materials and molecular modeling.

• Deep Learning Basics 2023/24

  e-Learning Courses

  • K. Draszawka

  A course about the basics of deep learning intended for students of Computer Science. It includes an introduction to supervised machine learning, the architecture of basic artificial neural networks and their training algorithms, as well as more advanced architectures (convolutional networks, recurrent networks, transformers) and regularization and optimization techniques.