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Machine Learning with scikit-learn
- ColinWright 13y agoI cannot tell you how much I hate these drip-feed presentations. There isn't even an indication of how long it goes for. The early stuff is obvious (for me) - how many times do I have to click to get to the interesting bits? There might be some great stuff here, but many of your potential audience will never find out, because they'll give up.
- jkldotio 13y agoMost reveal.js based presentations you can press "esc" and get a slide overview, although that's of limited use. Slides in general are simply not as useful as a reasonable web page if they don't have the presentation along with them in audio or video. It's a peculiar part of HN culture to link to just slides, I don't know of any other group that does it. A far better introduction to sci-kit learn is the project's examples page, http://scikit-learn.org/stable/auto_examples/index.html http://scikit-learn.org/stable/auto_examples/index.html, which you can use to get example code and data to generate each type of graph. The documentation on the rest of the site is also of very high quality.
- aidos 13y agoI was going to say that it's not really that much of a problem - until I got to page 7 and couldn't scroll down to see the code because I'm on a laptop. Instead I have to Select All - Copy - Paste into editor. That's unforgivable. Edit: on closer inspection it's just the last couple of lines that I'm missing - but it's still very annoying.
- sjtgraham 13y agoI know what I'm doing tonight. Great idea including sample data to play with in the library! Is that the MNIST data set?
- dhammack 13y agoHere's how to get MNIST, from the sklearn docs: >>> from sklearn.datasets import fetch_mldata >>> mnist = fetch_mldata('MNIST original', data_home=custom_data_home) I think the handwritten digits dataset used in the presentation is just a subset of MNIST; MNIST is 28x28 and the handwritten digits are 8x8.
- gallamine 13y agoHere's a link to the other available sample datasets: http://scikit-learn.org/stable/datasets/index.html http://scikit-learn.org/stable/datasets/index.html
- blauwbilgorgel 13y agoAndreas Mueller is one of the core devs of scikit learn. He is active on Kaggle.com too. For more practical ML projects see: https://github.com/amueller https://github.com/amueller
- toisanji 13y agoHe has only done one competition as noted in his slides.
- tucson 13y agoI'd like to know more about slide 6: http://amueller.github.io/sklearn_tutorial/#/6 http://amueller.github.io/sklearn_tutorial/#/6 Why the [Classification][100K sample?] checkpoint? And more info in general about this whole cheat-sheet.
- IanCal 13y agoSGD is fast but not necessarily more accurate. If you've got lots and lots of data, then a simple yet fast approach is likely to be a good choice to start with.
- tucson 13y agoThanks. I also found a blog post that figured the diagram, with some background infos: http://peekaboo-vision.blogspot.de/2013/01/machine-learning-cheat-sheet-for-scikit.html http://peekaboo-vision.blogspot.de/2013/01/machine-learning-...
- alok-g 13y agoAnd a prior HN discussion: https://news.ycombinator.com/item?id=5831512 https://news.ycombinator.com/item?id=5831512 There are many others too, but with few points and comments