对深度学习感兴趣的小伙伴们：个人认为最完整的深度学习教材，从数值分析基本概念开始，直到流行的各种模型的深度分析。
各章目录如下：


• Notation: Zhang Yuanhang.
• Chapter 1, : Yusuf Akgul, Sebastien Bratieres, 1 Introduction Samira Ebrahimi,

Charlie Gorichanaz, Brendan Loudermilk, Eric Morris, Cosmin Parvulescu
and Alfredo Solano.
• Chapter 2, : Amjad Almahairi, Nikola 2 Linear Algebra Banić, Kevin Bennett,
Philippe Castonguay, Oscar Chang, Eric Fosler-Lussier, Andrey Khalyavin,
Sergey Oreshkov, István Petrás, Dennis Prangle, Thomas Rohée, Gitanjali
Gulve Sehgal, Colby Toland, Alessandro Vitale and Bob Welland.
• Chapter 3, Probability and Information Theory: John Philip Anderson, Kai
Arulkumaran, Vincent Dumoulin, Rui Fa, Stephan Gouws, Artem Oboturov,
Antti Rasmus, Alexey Surkov and Volker Tresp.
• Chapter 4, Numerical Computation: Tran Lam AnIan Fischer and Hu
Yuhuang.
• Chapter 5, Machine Learning Basics: Dzmitry Bahdanau, Justin Domingue,
Nikhil Garg, Makoto Otsuka, Bob Pepin, Philip Popien, Emmanuel Rayner,
Peter Shepard, Kee-Bong Song, Zheng Sun and Andy Wu.
• Chapter 6, Deep Feedforward Networks: Uriel Berdugo, Fabrizio Bottarel,
Elizabeth Burl, Ishan Durugkar, Jeff Hlywa, Jong Wook Kim, David Krueger
and Aditya Kumar Praharaj.
• Chapter 7, Regularization for Deep Learning: Morten Kolbæk, Kshitij Lauria,
Inkyu Lee, Sunil Mohan, Hai Phong Phan and Joshua Salisbury.
• Chapter 8, Optimization for Training Deep Models: Marcel Ackermann, Peter
Armitage, Rowel Atienza, Andrew Brock, Tegan Maharaj, James Martens,
Kashif Rasul, Klaus Strobl and Nicholas Turner.
• Chapter 9, Convolutional Networks: Martín Arjovsky, Eugene Brevdo, Konstantin
Divilov, Eric Jensen, Mehdi Mirza, Alex Paino, Marjorie Sayer, Ryan
Stout and Wentao Wu.

• Chapter 10, Sequence Modeling: Recurrent and Recursive Nets: Gökçen
Eraslan, Steven Hickson, Razvan Pascanu, Lorenzo von Ritter, Rui Rodrigues,
Dmitriy Serdyuk, Dongyu Shi and Kaiyu Yang.
• Chapter 11, Practical Methodology: Daniel Beckstein.
• Chapter 12, Applications: George Dahl, Vladimir Nekrasov and Ribana
Roscher.
• Chapter 13, Linear Factor Models: Jayanth Koushik.

• Chapter 15, Representation Learning: Kunal Ghosh.
• Chapter 16, Structured Probabilistic Models for Deep Learning: Minh Lê
and Anton Varfolom.
• Chapter 18, Confronting the Partition Function: Sam Bowman.
• Chapter 19, Approximate Inference: Yujia Bao.
• Chapter 20, Deep Generative Models: Nicolas Chapados, Daniel Galvez,
Wenming Ma, Fady Medhat, Shakir Mohamed and Grégoire Montavon.
• Bibliography: Lukas Michelbacher and Leslie N. Smith.