Neural network references (textbooks, online courses) for beginners I want to learn Neural Networks.  I am a Computational Linguist. I know statistical machine learning approaches and can code in Python. 
I am looking to start with its concepts, and know one or two popular models which may be useful from a Computational Linguistics perspective.
I browsed the web for reference and found a few books and materials.


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*Ripley, Brian D. (1996) Pattern Recognition and Neural Networks, Cambridge

*Bishop, C.M. (1995) Neural Networks for Pattern Recognition, Oxford: Oxford University Press.

*some links, like this thesis, these course notes (University of Toronto Psychology Department), these course notes (University of Wisconsin Computer Science) and this slideshow (Facebook Research).
Coursera courses are generally nice, if anyone knows anything relevant from them. I prefer materials with lucid language and ample examples. 
 A: http://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html
http://neuralnetworksanddeeplearning.com/
This has been my favorite resources. Started with the Stanford machine learning course, but prefer reading over lectures. Especially because the readings are example-based.
A: You're in luck! There are an amazing number of resources available at the moment. In particular, you could look at:


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*a Coursera course starting soon

*a recently published online textbook by some of the leaders in the field (Goodfellow, Bengio and Courville)

*these lecture notes, and this overview, which are more oriented towards natural language processing

*a set of blog posts with beautiful visualizations by Chris Olah

*two well-supported toolkits with python interfaces and online tutorials: Tensorflow and Theano
A: Neural Networks and Deep Learning is an approachable starting-point.

Neural Networks and Deep Learning is a free online book. The book will teach you about:
Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data
Deep learning, a powerful set of techniques for learning in neural networks
Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you many of the core concepts behind neural networks and deep learning.

A: Main references:
Courses on deep learning:


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*Andrew Ng's course on machine learning has a nice introductory section on neural networks.

*Geoffrey Hinton's course: Coursera Neural Networks for Machine Learning (fall 2012)

*Michael Nielsen's free book Neural Networks and Deep Learning

*Yoshua Bengio, Ian Goodfellow and Aaron Courville wrote a book on deep learning (2016)

*Hugo Larochelle's course (videos + slides) at Université de Sherbrooke

*Stanford's tutorial (Andrew Ng et al.) on Unsupervised Feature Learning and Deep Learning

*Oxford's ML 2014-2015 course

*NVIDIA Deep learning course (summer 2015)

*Google's Deep Learning course on Udacity (January 2016)
NLP-oriented:


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*Stanford CS224d: Deep Learning for Natural Language Processing (spring 2015) by Richard Socher

*Tutorial given at NAACL HLT 2013: Deep Learning for Natural Language Processing (without Magic) (videos + slides)
Vision-oriented:


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*CS231n Convolutional Neural Networks for Visual Recognition by Andrej Karpathy (a previous version, shorter and less polished: Hacker's guide to Neural Networks).


Toolkit-specific tutorials:


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*DL4J (Java): http://deeplearning4j.org/documentation.html

*Theano  (Python, Y. Bengio): http://deeplearning.net/

*Machine Learning with Torch7 (Lua, LeCun): http://code.madbits.com/wiki/doku.php

*H2O Deep Learning (Java): http://0xdata.com/product/deep-learning/ 

*Caffee (C++, UCB): http://caffe.berkeleyvision.org/ 

*Nervana’s Deep Learning Course
A: For fast learning I would choose:
This Deep Learning lecture from the great teacher-researcher Nando de Freitas:
https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/
For practical programming theory understanding in Python this material from Andrej Karpathy:
http://cs231n.github.io/
And for NLP:
https://arxiv.org/abs/1510.00726
