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:

Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Introduction to Scientific Programming with Python - Joakim Sundnes
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Learning scikit-learn Machine Learning in Python - Raul Garreta & Guillermo Moncecchi
Grokking Deep Learning - MEAP v10 - Andrew W.Trask
Deep Learning from Scratch - Building with Python form First Principles - Seth Weidman
Deep Learning with Hadoop - Dipayan Dev
Natural Language Processing with Python - Steven Bird & Ewan Klein & Edward Loper
Deep Learning in Python - LazyProgrammer
Python Machine Learning - Sebastian Raschka
Neural Networks and Deep Learning - Charu C.Aggarwal
Practical computer vision applications using Deep Learning with CNNs - Ahmed Fawzy Gad
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Deep Learning with Theano - Christopher Bourez
Introducing Data Science - Davy Cielen & Arno D.B.Meysman & Mohamed Ali
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Applied Text Analysis with Python - Benjamin Benfort & Rebecca Bibro & Tony Ojeda
Artificial Intelligence with an introduction to Machine Learning second edition - Richar E. Neapolit...
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Data Science and Big Data Analytics - EMC Education Services
Introduction to the Math of Neural Networks - Jeff Heaton
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Coding Theory - Algorithms, Architectures and Application
Introduction to Machine Learning with Python - Andreas C.Muller & Sarah Guido
Building Machine Learning Systems with Python - Willi Richert & Luis Pedro Coelho
Python Machine Learning Eqution Reference - Sebastian Raschka
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Deep Learning for Natural Language Processing - Jason Brownlee
Python for Programmers with introductory AI case studies - Paul Deitel & Harvey Deitel
Python Data Structures and Algorithms - Benjamin Baka
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Python Data Analytics with Pandas, NumPy and Matplotlib - Fabio Nelli