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Kursplan
- Maskinlæringsbegrensninger Maskinlæring, ikke-lineære kartlegginger Nevrale nettverk Ikke-lineær optimalisering, Stokastisk/MiniBatch-gradient Anstendig ryggutbredelse Dyp sparsom koding Sparse Autoencoders (SAE) Convolutional Neural Networks (CNNs) Suksesser: Descriptor Matching Stereo-Aoidance for Obst [2 ] Pooling og invarians Visualisering/Dekonvolusjonelle nettverk Gjentakende nevrale nettverk (RNN) og deres optimering Applikasjoner til NLP RNN fortsetter, Hessian-Free Optimization Språkanalyse: ord/setningsvektorer, parsing, sentimentanalyse osv. Probabilistiske grafiske modeller Hopfield machine Nets, Boltzmann Deep Belief Nets, stablede RBM-applikasjoner til NLP, positur og aktivitetsgjenkjenning i videoer Nylige fremskritt nevrale turingmaskiner i stor skala for læring
Krav
God forståelse av Machine Learning. Minst teoretisk kunnskap om Deep Learning.
28 timer
Testimonials (4)
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Kurs - Advanced Deep Learning
Doing exercises on real examples using Keras. Mihaly totally understood our expectations about this training.
Paul Kassis
Kurs - Advanced Deep Learning
The exercises are sufficiently practical and do not need a high knowledge in Python to be done.
Alexandre GIRARD
Kurs - Advanced Deep Learning
The global overview of deep learning