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Deep Learning: Common Practices - Part 4 (WS 20/21)

Andreas Maier
8.07K subscribers
675 views 4 years ago ERLANGEN
Deep Learning: Common Practices - Part 4 This video discusses how to evaluate deep learning approaches. For reminders to watch the new video follow on Twitter   / maier_ak   or LinkedIn   / andreas-maier-a6870b1a6  . Interested in joining our network? Get reminders for talks, videos and job offers related to machine learning here: https://lme.tf.fau.de/lab/join-the-pa... Full Transcript (Summer 2020) https://towardsdatascience.com/common... Video References: Lex Fridman's Channel    / @lexfridman   Further Reading: A gentle Introduction to Deep Learning https://www.sciencedirect.com/science... Further Free Deep Learning Ressources (including exercises) https://lme.tf.fau.de/teaching/free-d... References: [1] M. Aubreville, M. Krappmann, C. Bertram, et al. “A Guided Spatial Transformer Network for Histology Cell Differentiation”. In: ArXiv e-prints (July 2017). arXiv: 1707.08525 [cs.CV]. [2] James Bergstra and Yoshua Bengio. “Random Searc …...more
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Deep Learning: Common Practices - Part 4 (WS 20/21)
20Likes
675Views
2020Dec 3
Deep Learning: Common Practices - Part 4 This video discusses how to evaluate deep learning approaches. For reminders to watch the new video follow on Twitter   / maier_ak   or LinkedIn   / andreas-maier-a6870b1a6  . Interested in joining our network? Get reminders for talks, videos and job offers related to machine learning here: https://lme.tf.fau.de/lab/join-the-pa... Full Transcript (Summer 2020) https://towardsdatascience.com/common... Video References: Lex Fridman's Channel    / @lexfridman   Further Reading: A gentle Introduction to Deep Learning https://www.sciencedirect.com/science... Further Free Deep Learning Ressources (including exercises) https://lme.tf.fau.de/teaching/free-d... References: [1] M. Aubreville, M. Krappmann, C. Bertram, et al. “A Guided Spatial Transformer Network for Histology Cell Differentiation”. In: ArXiv e-prints (July 2017). arXiv: 1707.08525 [cs.CV]. [2] James Bergstra and Yoshua Bengio. “Random Search for Hyper-parameter Optimization”. In: J. Mach. Learn. Res. 13 (Feb. 2012), pp. 281–305. [3] Jean Dickinson Gibbons and Subhabrata Chakraborti. “Nonparametric statistical inference”. In: International encyclopedia of statistical science. Springer, 2011, pp. 977–979. [4] Yoshua Bengio. “Practical recommendations for gradient-based training of deep architectures”. In: Neural networks: Tricks of the trade. Springer, 2012, pp. 437–478. [5] Chiyuan Zhang, Samy Bengio, Moritz Hardt, et al. “Understanding deep learning requires rethinking generalization”. In: arXiv preprint arXiv:1611.03530 (2016). [6] Boris T Polyak and Anatoli B Juditsky. “Acceleration of stochastic approximation by averaging”. In: SIAM Journal on Control and Optimization 30.4 (1992), pp. 838–855. [7] Prajit Ramachandran, Barret Zoph, and Quoc V. Le. “Searching for Activation Functions”. In: CoRR abs/1710.05941 (2017). arXiv: 1710.05941. [8] Stefan Steidl, Michael Levit, Anton Batliner, et al. “Of All Things the Measure is Man: Automatic Classification of Emotions and Inter-labeler Consistency”. In: Proc. of ICASSP. IEEE - Institute of Electrical and Electronics Engineers, Mar. 2005.

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Andreas Maier

8.07K subscribers
66
Deep Learning Lecture Winter 20/21
by Andreas Maier
Lex Fridman
4.81M subscribers