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I wish someone would come up with a tutorial for ML for the mathematically challenged. Something more practical and less theoretical.
https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX...
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This strategy is equally effective for most things in life.
> This strategy is equally effective for most things in life. Not if we understand 'effective' to also mean 'cost & time effective' You'd learn a lot by building a nuclear reactor from first principles, but it's not the most effective way to develop an intuition about how one operates.
I wish someone would come up with a tutorial for ML for the mathematically challenged. Something more practical and less theoretical.
1) Brandon Rohrer, now Data Scientist at Facebook, has a few great talks, including one on Bayes' Theorem/Bayesian Inference - https://m.youtube.com/playlist?list=PLVZqlMpoM6kbaeySxhdtgQP...
2) When asking future data scientists what tutorials for ML/NNs they like, they have usually found http://machinelearningmastery.com/ through Google and swear by it.
3) Josh Gordon, Developer Advocate at Google, has some simple ML/DL videos up in a 'Recipes' playlist: https://m.youtube.com/playlist?list=PLOU2XLYxmsIIuiBfYad6rFY...
If you want to just step through other people's code, you can do that too. Disclaimer: I put the below list together and it's not for ML broadly but for DL. That said if you want to run some examples fast and see the output, a number of folks have made that work for you -
I for one was floored to find great iOS examples (admittedly now deprecated for iOS 11). But If you have an iPhone with Metal (5s and up) Matthijs Hollemans - who wrote the iOS Apprentice at Ray Wenderlich - has Inception, YOLO, and MobileNets pre-trained and ready to go using Xcode, and it's fun to watch them work on your phone - https://medium.com/@SamPutnam/deep-learning-download-and-run...
I wish someone would come up with a tutorial for ML for the mathematically challenged. Something more practical and less theoretical.
To understand ML you've got to have at least a basic understanding of the math. And it's really not that difficult, especially if you find the right class/book/professor/etc.
The problem is that there are a ton of terrible writers and instructors out there.
I think it's just as important to ignore the terrible stuff (pretty much any blog post on ML) as it is to learn from the good stuff (e.g. Ng's ML course).
I wish someone would come up with a tutorial for ML for the mathematically challenged. Something more practical and less theoretical.
Never use predicting stock prices as an example of anything. Pick literally anything other than stock prices to display confidence bounds. This helpful example diagram violates the EMH even worse by showing a large predictable directional change. You might as well illustrate physics with a chart of bowling balls falling upward.
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Honestly, the best thing you can do is try to implement your own shitty neural net with only Python + Numpy, from scratch, with only a basic understanding of the math. It will make most of the math very concrete very fast.
Naive question : is this strategy equally effective for all ML models?
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Honestly, the best thing you can do is try to implement your own shitty neural net with only Python + Numpy, from scratch, with only a basic understanding of the math. It will make most of the math very concrete very fast.
Cannot upvote this enough. I think this is the only way to really grok backpropagation. The hours of staring at the update formula till your eyes glaze over the subscripts and superscripts and the summations would not give you as good an understanding as implementing a toy neural net with just a single hidden layer. Its actually a whole lot easier than parsing those low-level notation. It can be done better with high…
Earlier quoted context omitted.
Honestly, the best thing you can do is try to implement your own shitty neural net with only Python + Numpy, from scratch, with only a basic understanding of the math. It will make most of the math very concrete very fast.
Good idea! I'll give it a go
I learned nothing from impl my own other than why all NN libs break when you input values > 1.
Any decent NN lib will be way better that whatever you could write in a week full time.
Pick your fav lang, find the most used NN lib and try a kaggle competition.
If you can get to about rank 50% your training is complete.