Suyash suyash

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Octave CheatSheet

GNU Octave is a high-level interpreted language, primarily intended for numerical computations.
(via GNU Octave)

Hint: I also mad an octave docset for Dash: https://github.com/obstschale/octave-docset

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	Function::property = (prop, desc) ->
	Object.defineProperty @prototype, prop, desc

	class Person
	constructor: (@firstName, @lastName) ->
	@property 'fullName',
	get: -> "#{@firstName} #{@lastName}"
	set: (name) -> [@firstName, @lastName] = name.split ' '

	p = new Person 'Leroy', 'Jenkins'

	// Copyright (c) 2017 Ismael Celis

	// Permission is hereby granted, free of charge, to any person obtaining a copy
	// of this software and associated documentation files (the "Software"), to deal
	// in the Software without restriction, including without limitation the rights
	// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
	// copies of the Software, and to permit persons to whom the Software is
	// furnished to do so, subject to the following conditions:

	// The above copyright notice and this permission notice shall be included in all

	/**
	* Fancy ID generator that creates 20-character string identifiers with the following properties:
	*
	* 1. They're based on timestamp so that they sort after any existing ids.
	* 2. They contain 72-bits of random data after the timestamp so that IDs won't collide with other clients' IDs.
	* 3. They sort lexicographically (so the timestamp is converted to characters that will sort properly).
	* 4. They're monotonically increasing. Even if you generate more than one in the same timestamp, the
	* latter ones will sort after the former ones. We do this by using the previous random bits
	* but "incrementing" them by 1 (only in the case of a timestamp collision).
	*/

	"""An example of how to use tf.Dataset in Keras Model"""
	import tensorflow as tf # only work from tensorflow==1.9.0-rc1 and after

	_EPOCHS = 5
	_NUM_CLASSES = 10
	_BATCH_SIZE = 128

	def training_pipeline():
	# #############
	# Load Dataset