This is really cool and inspiring!
Thank you! The response here has been amazing. :)
Machine Learning from scratch: Bare bones implementations in Python
41–50 of 67 posts
Re: Machine Learning from scratch: Bare bones implementations in Python
#42This is awesome!
Glad you like it!
Re: Machine Learning from scratch: Bare bones implementations in Python
#43Very nice project! Very very useful for a ML beginner like myself. Thank you very much !
Re: Machine Learning from scratch: Bare bones implementations in Python
#44Amazing, thanks for sharing :)
Re: Machine Learning from scratch: Bare bones implementations in Python
#45Fuck you
Re: Machine Learning from scratch: Bare bones implementations in Python
#46Re: Machine Learning from scratch: Bare bones implementations in Python
#47Great resource, but it could be a phenomenal resource if you documented each method and explained how and why it does what it does.
Don't get me wrong, having working code to play with is key, but when you don't fully grasp the concepts behind it, an explanation can become so valuable.
That being said, you've included names, so research can be done. Great work and I hope you're enjoying it!
Re: Machine Learning from scratch: Bare bones implementations in Python
#48In your RandomForest implementation, on the line in fit() where you're building the training subsets to give to each tree, it appears that your bagging approach doesn't use 'sampling with replacement' strategy.
idx = np.random.choice(range(n_features), size=self.max_features, replace=False)
Re: Machine Learning from scratch: Bare bones implementations in Python
#49This could become a fantastic resource for anybody who is teaching machine learning.
One vital improvement suggestion to make that path attractive would be if the Jupyter notebook format were used. It would be easier to add more documentation and references.
But in any case, thanks for sharing!
Re: Machine Learning from scratch: Bare bones implementations in Python
#50Earlier quoted context omitted.
GMRES is definitely my go-to these days. Though it is worth noting that direct methods do have a benefit of letting you quickly solve many successive linear problems involving the same matrix, but different right-hand sides. But iterative methods scale very well for large sparse problems that they are very often the only tool to consider. Block Krylov methods are a thing , but I haven't experimented with them yet.
For solving linear systems there are recent mind-blowing methods by R. M. Gower and P. Richtarik: https://arxiv.org/abs/1506.03296 http://www.maths.ed.ac.uk/~richtarik/papers/SDA.pdf Plus R. M. Gower is fantastically nice and enthusiastic so there's that And thanks for the link !
Has anyone implemented them in production? I read their paper a while ago, but thought that the dual methods they showed might have practical limitations (like insane cache misses, compared to more standard methods)