itsmuriuki

def square_root (n,d) #number and error difference
	guess = n / 2.0  #half the guess since square root cant be more than half
	while (((guess *guess) - n).abs > d)
		guess = refine(guess, n)
	end
	puts guess
end

def refine (guess, n)
	guess = (guess + (n / guess)) / 2.0

itsmuriuki

def square_root (n)
	guess = n / 2.0
	while (((guess *guess) - n).abs > 0.000000001)
		guess = refine(guess, n)
	end
	return guess
end

def refine (guess, n)

itsmuriuki

def max_min(arr):
	mini=arr[0]
	maxi=arr[0]

	for x in arr:
		if maxi<x: 
			maxi=x
		if mini>x:
			mini=x
	max_and_min=[maxi,mini]

itsmuriuki

def wierd_sort (arr): #defining the length of the array
	wierd =[] #Empty arry to store sorted arr
	myarr = arr[:] #makes a copy of the array
	myarr = sorted(myarr) #sorts array
	i = 0 
	while i < len(myarr): #goes through the lenngth of the array
	 if len(myarr) > 1:
		wierd.append(myarr[0]) #appends the first digit into the new arr
		wierd.append(myarr[-1]) #appends the last digit into the new arr
		myarr.remove(myarr[0]) #removes  the first digit from a copy of the initial array

itsmuriuki

arra = [1,2,3]


def roots_of_eqn(coeffs)
    a = coeffs[0]
    b = coeffs[1]
    c = coeffs[2]

    d = (b * b) - (4 * a * c)
    if d <= 0

itsmuriuki

def sort_playlist(arr):
	arr = arr[:]
	for num_pass in range(len(arr)-1,0,-1):
		for i in range(num_pass):
			if arr[i][1] > arr[i+1][1]:
				arr[i], arr[i+1] = arr[i+1],arr[i]
	return arr			

def select_songs(playlist,time):
	sorted_playlist = sort_playlist

itsmuriuki

#checking python version
python3 --version     # should return 'Python 3.4.0'

sudo apt-get install python3-pip

sudo pip3 install pymongo #installing pymongo 

itsmuriuki

Verifying my Blockstack ID is secured with the address 1Fi57n2HTNgRerKqEm1KD9bYSSnJZkt4wF https://explorer.blockstack.org/address/1Fi57n2HTNgRerKqEm1KD9bYSSnJZkt4wF

itsmuriuki

itsmuriuki

The list by @goodfellow_ian, as per Twitter discussion with @timnitGebru: https://twitter.com/timnitGebru/status/968242968007200769

1. Progressive GANs:
   • Video: https://www.youtube.com/watch?v=XOxxPcy5Gr4
   • Link: https://arxiv.org/abs/1710.10196
   • Description: Probably the highest quality images so far
2. Spectral normalization:
   • Link: https://openreview.net/forum?id=B1QRgziT-&noteId=BkxnM1TrM
   • Description: Got GANs working on lots of classes, which has been hard
3. Projection discriminator