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    Yogesh Narayan Singh
    @yogids
    Also for n categorical to n binary columns... are we trying to make dummy variables here?
    skklogw7
    @skklogw7
    Hey all!
    Keith Aumiller
    @keithaumiller
    No worries.
    I ended up just using Fork instead of trying to do some complicated multithreading stuff.
    ;)
    Keith Aumiller
    @keithaumiller
    Hey Guys
    I'm going to fire up a cyclops.io video chat
    if anybody cares to join me
    Keith Aumiller
    @keithaumiller
    K, I'm out. GN
    Viral Chavda
    @virus123
    helloo
    vamsilnm
    @vamsilnm
    Hello guys can any one shed some light on how to build a chat bot which is domain based like say for example for air lines ticket booking using deep learning
    like what are the steps
    or so /
    ?
    Keith Aumiller
    @keithaumiller
    Sorry Vamsilnm
    just saw this.
    Natural language processing is the field it is in.
    I haven't built one myself, but this is a good place to start:
    tedkimzikto
    @tedkimzikto
    Hello
    Keith Aumiller
    @keithaumiller
    Hey
    vaibhav541
    @vaibhav541

    I was trying to make a program for image compression using k means clustering
    Can someone tell me what’s wrong with this code?
    from scipy import misc
    import numpy as np
    from scipy.misc import toimage
    img=misc.imread('bird_small.png')
    img=img.reshape((16384,3))

    def findc(X,incd) :
    c=[]

    for j in range(0,16384):
        k1 = []
        for i in range(0,16):
    
            k=X[j]-incd[i]
            k1.append(k.dot(k.transpose()))
        print(j)
    
        c.append(np.argmax(k1))
    
    return c

    def findu(X,u):
    u=np.zeros((16,3))
    a=np.zeros(16)
    for j in range(0,16384):
    for i in range(0,16):
    if(c[j]==i):
    u[i]=u[i]+X[j]
    a[i]=a[i]+1

    newc=[]
    for i in range(0,16):
        newc.append(u[i]/a[i])
    return newc

    incd = np.random.randint(np.size(img,axis=0), size=16)
    print(np.size(img,axis=0))
    incd = img[incd, :]
    incd = incd.reshape((16, 3))
    print(incd)

    for _ in range(0,10):
    c=findc(img,incd)
    prevcd=incd
    incd=findu(img,c)

    for j in range(0,16384):
    for i in range(0, 16):
    if (c[j] == i):
    img[j]=incd[i]

    img.reshape((128,128,3))
    toimage(img).show()

    Pankaj-Sakariya
    @Pankaj-Sakariya
    Hello Anyone has implemented K-anonymity with clustering?
    Keith Aumiller
    @keithaumiller
    I've done a lot of clustering work, what do you mean specifically about k-anonymity?
    Pankaj-Sakariya
    @Pankaj-Sakariya
    I want to implement K- anonymity with approach of clustering method.
    This is the algorithm which I have implemented using Java.
    0.K-anonymity  & Enhanced Clustering.pdf