mikeskim

The way I use Python in machine learning is quite different from how many others in competitive ML use Python. I use Python purely for Python 2.7 with Pypy and try not to touch or use numpy,scipy,pandas,etc. R's data.table is possibly faster than Python's numpy/scipy/pandas. I think anyone claiming Python because of numpy/scipy/pandas is really being mislead. You should be using Python in spite of the need to rely upon numpy/scipy/pandas. If you really need numpy/scipy/pandas just use R and data.table which is amazingly fast. I think Python is really great because of Pypy and the strength of the standard Python library.

It is a fact that students at top tech schools (think Caltech, MIT, Harvey Mudd, etc.) study longer compared to students at comparable liberal arts schools. I believe it takes time and practice to get good at anything. Students at tech schools in tech majors just spend more time practicing their craft. Obviously that doesn't mean some English major can't pick up math, computer science and physics later in life, but such people are the outliers.

I wish academics would publish pure python implementations of their "new" algorithms. Standard python with Pypy is enough for speed of development and runtime.

The biggest thing about t-SNE is that it's been used in competitive machine learning for quite a long time successfully by many different people because it's on R via CRAN and Python via sklearn. LargeVis has potential, but it could also be not so useful like the vast majority of academic work.

I would not be surprised if most academic CS research is not reproducible. This is true for many other fields outside of CS, and I've seen it first hand in machine learning. It's a problem but it's also just how things are.

I know people who work at those research divisions (they work with neural networks) without a PhD. So I just falsified your claim.

This is incorrect in almost every way. When you have 2^m independent observations that you can use to cross validate (where m is very large), overfitting is exceptionally difficult almost regardless of the number of features you have. Overfitting typically occurs when the number of data points is small in magnitude overall and is small compared to the number of features and the observations are not iid.

If people would just learn to resample (cross validate, use subsampling or the bootstrap), we wouldn't be having this pointless discussion at all.

I'd like feedback on if this algorithm is new or has been published before. I think it's new, but I've been wrong before on these types of issues. Thanks: Mike.

compete at www.kaggle.com defeat is the best teacher.

You wouldn't find it intuitive if your first language was Scheme.