4 ms·

Ask HN: Where to begin learning about Neural Nets

4 points by MollyR 11y ago

I want to start learning more about machine learning, neural nets, and how to apply them practically. I'm looking for advice, links, recommendations. Any thing python related would be a huge plus too.

digisth 11y ago
The below resources are the ones I used when I started to learn about DL/NNs. Some of them are focused specifically on certain applications, but I found them helpful, too. Basic NNs: http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/ http://www.wildml.com/2015/09/implementing-a-neural-network-... (a whole series, all worth reading) https://gist.github.com/sthware/c47824c116e6a61a56d9 https://gist.github.com/sthware/c47824c116e6a61a56d9 (my code based on the above) http://iamtrask.github.io/2015/07/27/python-network-part2/ http://iamtrask.github.io/2015/07/27/python-network-part2/ http://rolisz.ro/2013/04/18/neural-networks-in-python/ http://rolisz.ro/2013/04/18/neural-networks-in-python/ ML: http://cs229.stanford.edu/materials.html http://cs229.stanford.edu/materials.html http://onlinestatbook.com/2/index.html http://onlinestatbook.com/2/index.html DL in general, RNNs, RNTS, CNNs, some others: http://cs224d.stanford.edu/syllabus.html http://cs224d.stanford.edu/syllabus.html http://cs231n.stanford.edu/syllabus.html http://cs231n.stanford.edu/syllabus.html http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ http://mattmazur.com/2015/03/17/a-step-by-step-backpropagati... http://arxiv.org/pdf/cs/0205070.pdf http://arxiv.org/pdf/cs/0205070.pdf http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://karpathy.github.io/2015/05/21/rnn-effectiveness/ http://karpathy.github.io/2015/05/21/rnn-effectiveness/ http://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf http://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf http://alexdavies.net/talks/ http://alexdavies.net/talks/ http://www.socher.org/index.php/Main/SemanticCompositionalityTh http://www.socher.org/index.php/Main/SemanticCompositionalit... roughRecursiveMatrix-VectorSpaces http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/ http://colah.github.io/posts/2014-07-NLP-RNNs-Representation...
cpro 11y ago
My favorite tutorial is located here: http://www.ai-junkie.com/ann/evolved/nnt1.html http://www.ai-junkie.com/ann/evolved/nnt1.html It's easy to follow and not too theoretical. It's really down to earth and code/algorithm focused.

4 points by MollyR 11y ago

digisth 11y ago

The below resources are the ones I used when I started to learn about DL/NNs. Some of them are focused specifically on certain applications, but I found them helpful, too. Basic NNs: http://www.wildml.com/2015/09/implementing-a-neural-network-from-scratch/ http://www.wildml.com/2015/09/implementing-a-neural-network-... (a whole series, all worth reading) https://gist.github.com/sthware/c47824c116e6a61a56d9 https://gist.github.com/sthware/c47824c116e6a61a56d9 (my code based on the above) http://iamtrask.github.io/2015/07/27/python-network-part2/ http://iamtrask.github.io/2015/07/27/python-network-part2/ http://rolisz.ro/2013/04/18/neural-networks-in-python/ http://rolisz.ro/2013/04/18/neural-networks-in-python/ ML: http://cs229.stanford.edu/materials.html http://cs229.stanford.edu/materials.html http://onlinestatbook.com/2/index.html http://onlinestatbook.com/2/index.html DL in general, RNNs, RNTS, CNNs, some others: http://cs224d.stanford.edu/syllabus.html http://cs224d.stanford.edu/syllabus.html http://cs231n.stanford.edu/syllabus.html http://cs231n.stanford.edu/syllabus.html http://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ http://mattmazur.com/2015/03/17/a-step-by-step-backpropagati... http://arxiv.org/pdf/cs/0205070.pdf http://arxiv.org/pdf/cs/0205070.pdf http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://colah.github.io/posts/2015-08-Understanding-LSTMs/ http://karpathy.github.io/2015/05/21/rnn-effectiveness/ http://karpathy.github.io/2015/05/21/rnn-effectiveness/ http://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf http://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf http://alexdavies.net/talks/ http://alexdavies.net/talks/ http://www.socher.org/index.php/Main/SemanticCompositionalityTh http://www.socher.org/index.php/Main/SemanticCompositionalit... roughRecursiveMatrix-VectorSpaces http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/ http://colah.github.io/posts/2014-07-NLP-RNNs-Representations/ http://colah.github.io/posts/2014-07-NLP-RNNs-Representation...

cpro 11y ago

My favorite tutorial is located here: http://www.ai-junkie.com/ann/evolved/nnt1.html http://www.ai-junkie.com/ann/evolved/nnt1.html It's easy to follow and not too theoretical. It's really down to earth and code/algorithm focused.