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A collection of Kaggle solutions. Not very polished.

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- - 1-cleaned.png
  - 1.png
  - 4-cleaned.png
  - 4.png
  - 61-cleaned.png
  - 61.png
- clean.py
- README.md
- submit.py
- train.py
README.md

// repository documentation

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# Kaggle solutions

I've been using Kaggle as an excuse to learn techniques in machine
learning/artificial intelligence.

## Resources I've been learning from

Here are some primary resources I've been learning from (in rough
chronological order). For reference, I started from an extensive
programming background, a decent but rusty math background, and a
rudimentary background in machine learning.

- http://karpathy.github.io/2015/05/21/rnn-effectiveness/: It was fun
  to play with the released code, even though I didn't yet know what
  many of the parameters meant.

- http://www.pyimagesearch.com/2014/09/22/getting-started-deep-learning-python/:
  Didn't worry too much about the details of Deep Belief Networks,
  since I've been told those aren't behind the most recent advances in
  deep learning. However, I found a lot of value in actually getting a
  not-completely-black-boxed neural network up and running.

- http://neuralnetworksanddeeplearning.com/: It wasn't until I worked
  through this book that I really felt like I understood what was
  going on (or at least knew enough to have a sense of what I didn't
  know). I highly recommend going through the entire thing. I was
  particularly impressed by the author's ability to anticipate my
  confusions and objections, and also convey intuitions and
  motivations.

- https://class.coursera.org/ml-005/lecture: Andrew Ng's Machine
  Learning course. Contains helpful details on a number of topics I
  hadn't seen before. However, the course moves slower than I was
  hoping, but with the right cherry-picking it felt pretty useful.

- http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf: Useful for
  learning some practical tricks for getting better performance out of
  your neural network. The first half is very useful and readable
  (though I didn't work through all of the math). The second half
  seems less so: as they conclude the paper, "Classical second-order
  methods are impractical in almost all useful cases".

- http://deeplearning.net/tutorial/lenet.html: Decent introduction to
  convolutional neural networks. I wasn't previously familiar with
  convolutions and didn't fully understand it until I'd read
  http://www.songho.ca/dsp/convolution/convolution.html.

- http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf:
  Contains thorough background on recurrent neural networks, with many
  experiments and tricks for training your own.

- http://andrew.gibiansky.com/blog/machine-learning/conjugate-gradient/:
  This whole blog is great. It has good exposition on some of the more
  mathematically-involved techniques.

- http://arxiv.org/pdf/1211.5063v2.pdf: A better choice of activation
function and initialization scheme.

- http://deepdish.io/2015/02/24/network-initialization/: More recent
  overview of initialization techniques.

- http://yyue.blogspot.com/2015/01/a-brief-overview-of-deep-learning.html:
  Great overview of how to think about deep neural networks, and how
  to train them in practice.

- https://christopherolah.wordpress.com/: Many amazing blog posts
  which explore deep concepts in accessible ways.

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