Deep Learning - Study Circle
Studiecirkel i djupa neuralnät

FRT170F, 7.5 högskolepoäng

Gäller från och med: Autumn 2016
Beslutad av: Professor Thomas Johansson
Datum för fastställande: 2017-06-01

Allmänna uppgifter

Avdelning: Automatic Control
Kurstyp: Ren forskarutbildningskurs
Undervisningsspråk: English

Syfte

The study circle introduces doctoral students to recent advances in deep learning. Focus is on using deep learning in practice.

Mål

Kunskap och förståelse

För godkänd kurs skall doktoranden

• understand how deep networks can be constructed and trained.
• know typical components of a deep network such as RBMs, autoencoders, RNNs.
• understand how typical network and training parameters influence the performance of the networks and their training speed.

Färdighet och förmåga

För godkänd kurs skall doktoranden

• be able to construct and train a deep neural network using an existing software platform, such as Tensorflow, Theano or similar.
• be able to understand how to map a practical problem to a deep network architecture.
• read and understand research articles in the field of deep learning.
• be able to choose efficient network training methods.

Kursinnehåll

The content is based on the doctoral students' choice and active participations. Possible subtopics include Autoencoders, Convolutionan Networks, Structured Probabilistic Models, Restricted Boltzmann Machines, Recurrent and Recursive Nets, Deep Reinforcement Learning, Tensorflow, Deep Learning using GPUs, Stacked Denoising Autoencoders, DL for Natural Language Processing, Deconvolutionan networks, Optimization of Deep Networks.

Kurslitteratur

Goodfellow, B. & Courville: Deep Learning (manuscript).
Book manuscript: https://github.com/HFTrader/DeepLearningBook/blob/master/DeepLearningBook.pdf

Kursens undervisningsformer

Undervisningsformer: Föreläsningar, laborationer

Kursens examination

Examinationsformer: Inlämningsuppgifter, seminarieföredrag av deltagarna. For a passing grade the doctoral student should give a lecture on a deep learning topic and construct a suitable hand-in assignment for the other course participants. The student should also solve at least half of the exercises presented in the course and upload solutions and code to a common code repository.
Betygsskala: Underkänd, godkänd
Examinator:

Antagningsuppgifter

Förutsatta förkunskaper: The participants are assumed to have taken a course in basic machine learning or have corresponding knowledge.

Kurstillfällesinformation

Kontaktinformation och övrigt

Kursansvariga:
Hemsida: http://www.control.lth.se/Education/DoctorateProgram/deep-learning-study-circle.html