SubjectsSubjects(version: 875)
Course, academic year 2020/2021
Deep learning seminar - NPFL117
Title: Seminář z hlubokého učení
Guaranteed by: Institute of Formal and Applied Linguistics (32-UFAL)
Faculty: Faculty of Mathematics and Physics
Actual: from 2020
Semester: both
E-Credits: 3
Hours per week, examination: 0/2 C [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: not taught
Language: English, Czech
Teaching methods: full-time
Additional information:
Note: you can enroll for the course repeatedly
you can enroll for the course in winter and in summer semester
Guarantor: RNDr. Milan Straka, Ph.D.
Annotation -
Last update: T_UFAL (01.02.2017)
In recent years, deep neural networks have been used to solve complex machine-learning problems and have achieved significant state-of-the-art results in many areas. The whole field of deep learning has been developing rapidly, with new methods and techniques emerging steadily. The goal of the seminar is to follow the newest advancements in the deep learning field. The course takes form of a reading group – each lecture a paper is presented by one of the students. The paper is announced in advance, hence all participants can read it beforehand and can take part in the discussion of the paper
Course completion requirements -
Last update: RNDr. Milan Straka, Ph.D. (12.10.2017)

Students pass the course by presenting a research paper and by sufficient attendance at the courses. Considering the rules for completing the course, it is not possible to retry passing it.

Literature -
Last update: T_UFAL (01.02.2017)

Selected conference and journal papers on deep learning

Syllabus -
Last update: T_UFAL (01.02.2017)

Presentations of recent results in the deep learning field. The papers are presented by the participants, with the papers being announced in advance to allow purposeful discussion. The paper topics can span any deep learning application (image processing, natural language processing, speech processing, reinforcement learning, deep generative models, etc.) depending on the interest of the participants.

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