Deep Learning – Ian Goodfellow & Yoshua Bengio & Aaron Courville

We would like to thank those who commented on our proposal for the book and helped plan its contents and organization: Guillaume Alain, Kyunghyun Cho, Çağlar Gülçehre, David Krueger, Hugo Larochelle, Razvan Pascanu and Thomas Rohée.

We would like to thank the people who offered feedback on the content of the book itself. Some offered feedback on many chapters: Martín Abadi, Guillaume Alain, Ion Androutsopoulos, Fred Bertsch, Olexa Bilaniuk, Ufuk Can Biçici, Matko Bošnjak, John Boersma, Greg Brockman, Alexandre de Brébisson, Pierre Luc Carrier, Sarath Chandar, Pawel Chilinski, Mark Daoust, Oleg Dashevskii, Laurent Dinh, Stephan Dreseitl, Jim Fan, Miao Fan, Meire Fortunato, Frédéric Francis, Nando de Freitas, Çağlar Gülçehre, Jurgen Van Gael, Javier Alonso García, Jonathan Hunt, Gopi Jeyaram, Chingiz Kabytayev, Lukasz Kaiser, Varun Kanade, Asifullah Khan, Akiel Khan, John King, Diederik P. Kingma, Yann LeCun, Rudolf Mathey, Matías Mattamala, Abhinav Maurya, Kevin Murphy, Oleg Mürk, Roman Novak, Augustus Q. Odena, Simon Pavlik, Karl Pichotta, Eddie Pierce, Kari Pulli, Roussel Rahman, Tapani Raiko, Anurag Ranjan, Johannes Roith, Mihaela Rosca, Halis Sak, César Salgado, Grigory Sapunov, Yoshinori Sasaki, Mike Schuster, Julian Serban, Nir Shabat, Ken Shirriff, Andre Simpelo, Scott Stanley, David Sussillo, Ilya Sutskever, Carles Gelada Sáez, Graham Taylor, Valentin Tolmer, Massimiliano Tomassoli, An Tran, Shubhendu Trivedi, Alexey Umnov, Vincent Vanhoucke, Marco Visentini-Scarzanella, Martin Vita, David Warde-Farley, Dustin Webb, Kelvin Xu, Wei Xue, Ke Yang, Li Yao, Zygmunt Zając and Ozan Çağlayan.

Related posts:

Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Deep Learning with Keras - Antonio Gulli & Sujit Pal
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Deep Learning in Python - LazyProgrammer
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Deep Learning with Python - Francois Chollet
Python Machine Learning - Sebastian Raschka
Generative Deep Learning - Teaching Machines to Paint, Write, Compose and Play - David Foster
Deep Learning with Hadoop - Dipayan Dev
Deep Learning with Python - Francois Cholletf
Data Science and Big Data Analytics - EMC Education Services
An introduction to neural networks - Kevin Gurney & University of Sheffield
Hands-On Machine Learning with Scikit-Learn and TensorFlow - Aurelien Geron
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurelien Geron
Deep Learning Illustrated - A visual, Interactive Guide to Arficial Intelligence First Edition - Jon...
Deep Learning and Neural Networks - Jeff Heaton
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Coding Theory - Algorithms, Architectures and Application
Python Machine Learning Eqution Reference - Sebastian Raschka
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Deep Learning for Natural Language Processing - Jason Brownlee
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Pattern recognition and machine learning - Christopher M.Bishop
Machine Learning with spark and python - Michael Bowles