[rustyfe]

Could anyone recommend a starting point on Neural Networks for the uninitiated? The parts of this I understood were fascinating, but I quickly realized I was looking up every third word, and not really absorbing much.

If I could only read one thing to gain the technical grounding for this history, what should it be?

[trophygeek]

This was the one that flipped the lightbulb for me.

`Hacker's guide to Neural Networks` http://karpathy.github.io/neuralnets/

[trophygeek]

Actually, read that one 2nd. Start here: https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convn...

[WhoBeI]

That one was very good and links to a nice demo http://scs.ryerson.ca/~aharley/vis/conv/flat.html

[rustyfe]

Hey, the premise was I could only read one thing! I kid, much obliged.

[trophygeek]

lol. point

Read the 2nd one then.

[eddiepierce]

Karpathy's course notes for Stanford's Convolutional Neural Networks for Visual Recognition is a great intro [1].

It starts with with linear classification, then moves to neural nets, and then explains convolutional neural nets.

[1]: http://cs231n.github.io

[billconan]

this should get you started:

http://neuralnetworksanddeeplearning.com/

[vonnik]

[Disclosure: I wrote this.]

http://deeplearning4j.org/neuralnet-overview.html

Also, this book is coming:

https://www.amazon.com/Deep-Learning-Practitioners-Adam-Gibs...

[j_juggernaut]

How about the textbook http://www.deeplearningbook.org/.

It introduces you to some of the underlying principles which haven't changed much over time. I highly recommend it if you want to get deeper intuitions on the principles of CNN, LSTM/RNN, Restricted Boltzmann Machines etc. Also, Hinton's Coursera lectures, though not sure if you can access it anymore.

[Joof]

It was on academic torrents I believe.

[llSourcell]

https://www.youtube.com/watch?v=h3l4qz76JhQ

[ramblenode]

The other suggestions in this thread are quite good. I'll add "Machine Learning" by Murphy. It's not strictly about neural networks but it's an ML classic and a rigorous introduction to the subject that will give you a principled understanding of the statistical fundamentals. For actual NN implementation the Karpathy and Nielson sources are excellent.

[p1esk]

Definitely not the starting point for someone who has no clue. Murphy is probably the very last thing one would read before becoming an expert, and start publishing ML papers.

[imh]

This course is a great first step in learning about neural nets: https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearni...