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Ian Goodfellow

Alumni

Publications

Deep Learning. Das umfassende Handbuch
Ian Goodfellow
Yoshua Bengio
Aaron Courville
2018-10-09
(published)
www.semanticscholar.org
Deep Learning
Ian Goodfellow
Yoshua Bengio
Aaron Courville
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy… (see more) of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
2016-11-17
MIT Press eBooks (unknown)
openalex.org
Theano: A Python framework for fast computation of mathematical expressions
Rami Al-Rfou
Guillaume Alain
Amjad Almahairi
Christof Angermueller
Dzmitry Bahdanau
Nicolas Ballas
Frédéric Bastien
Justin Bayer
Anatoly Belikov
Alexander Belopolsky
Yoshua Bengio
Arnaud Bergeron
James Bergstra
Valentin Bisson
Josh Bleecher Snyder
Nicolas Bouchard
Nicolas Boulanger-Lewandowski
Xavier Bouthillier
Alexandre De Brébisson
Olivier Breuleux … (see 92 more)
Pierre-Luc Carrier
Kyunghyun Cho
Jan Chorowski
Paul Christiano
Tim Cooijmans
Marc-Alexandre Côté
Myriam Côté
Aaron Courville
Yann N. Dauphin
Olivier Delalleau
Julien Demouth
Guillaume Desjardins
Sander Dieleman
Laurent Dinh
Mélanie Ducoffe
Vincent Dumoulin
Samira Ebrahimi Kahou
Dumitru Erhan
Ziye Fan
Orhan Firat
Mathieu Germain
Xavier Glorot
Ian Goodfellow
Matt Graham
Caglar Gulçehre
Philippe Hamel
Iban Harlouchet
Jean-philippe Heng
Balázs Hidasi
Sina Honari
Arjun Jain
Sébastien Jean
Kai Jia
Mikhail Korobov
Vivek Kulkarni
Alex Lamb
Pascal Lamblin
Eric Larsen
César Laurent
Sean Lee
Simon Lefrancois
Simon Lemieux
Nicholas Léonard
Zhouhan Lin
Jesse A. Livezey
Cory Lorenz
Jeremiah Lowin
Qianli Ma
Pierre-Antoine Manzagol
Olivier Mastropietro
Robert T. McGibbon
Roland Memisevic
Bart Van Merriënboer
Vincent Michalski
Mehdi Mirza
Alberto Orlandi
Christopher Pal
Razvan Pascanu
Mohammad Pezeshki
Colin Raffel
Daniel Renshaw
Matthew Rocklin
Adriana Romero
Markus Roth
Peter Sadowski
John Salvatier
François Savard
Jan Schlüter
John Schulman
Gabriel Schwartz
Iulian Vlad Serban
Dmitriy Serdyuk
Samira Shabanian
Etienne Simon
Sigurd Spieckermann
S. Ramana Subramanyam
Jakub Sygnowski
Jeremie Tanguay
Gijs van Tulder
Joseph Turian
Sebastian Urban
Pascal Vincent
Francesco Visin
Harm de Vries
David Warde-Farley
Dustin J. Webb
Matthew Willson
Kelvin Xu
Lijun Xue
Li Yao
Saizheng Zhang
Ying Zhang
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (see more)ntly. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.
2015-12-31
arXiv (preprint)
doi.org
arxiv.org