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A Few Useful Things to Know about Machine Learning

The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.

1. Learning = Representation + Evaluation + Optimization

All machine learning algorithms have three components:

Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
Evaluation function tells how good the machine learning model is.
Optimisation is the method to search for the most optimal learning model.

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	তথাকথিত সালাফী আলেমদের আকিদাগত মতবিরোধ (পর্ব-১)◄█▓▒░


	ইসলামের মূল হলো বিশ্বাস। আল্লাহ সম্পর্কে বিশ্বাস।
	আল্রাহর নবী-রাসূল, ফেরেশতা ও পরকাল সম্পর্কে বিশ্বাস।
	█►https://facebook.com/notes/324360791046673
	তথাকথিত সালাফী আলেমদের আকিদাগত মতবিরোধ (পর্ব-২)◄█▓▒░
	আল্লাহ তায়ালা আরশে বসে আছেন (নাউযুবিল্লাহ)
	আরশে বসার ব্যাপারে ইহুদী আকিদা:
	█►https://facebook.com/notes/324487681033984

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	return dictionary


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	'''
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	Usage: infb.py user@domain.tld password
	infb.py

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