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    Petru-Daniel Tudosiu
    @danieltudosiu
    And aslso the model might be too small
    (I am a student so please take it as a grain of salt)
    Pavel Surmenok
    @surmenok
    The dataset is probably noisy. If I reduce dataset size to few hundred thousand examples I get training accuracy above 90%. But even if the dataset is noisy, can it lead to training error increasing over time? I've thought if model capacity is large enough training error should decrease to near 0 (memorize the training set), if capacity is small it should stay flat at some point.
    "Understanding Deep Learning Requires Rethinking Generalization" paper shows how neural networks can memorize even random labels
    Petru-Daniel Tudosiu
    @danieltudosiu
    Assuimg you reduce in a random manner, I can only assume that the noisy is made by a model to throw off other models (GAMs by Ian Goodfellow)
    Anisa
    @AnisaAl
    hey i just read quickly about machine learning course needed, there were discounts at udemy, i just registered myself : https://www.udemy.com/machinelearning/learn/v4/overview
    its limited time
    Utkarsh Rai
    @utkarshrai
    Just dropping in a hello in case I sleep and miss the chat again.
    Keith Aumiller
    @keithaumiller
    Sorry I couldn't make it Friday night guys, I was at the Machine Learning in Finance conference
    Feel free to read through my notes and if you have any questions, let me know.
    The Goldman Sachs Senior Data scientists I talked to was a really cool guy
    Keith Aumiller
    @keithaumiller
    Great story about how he went from sleeping in his car, to winning data hackathons in San Fran, to working at GS
    rawan la
    @rawan_la_twitter
    Hi, I want to classify a multi labeled data using deep learning techniques like CNN without building multiple classifier for each label.. when I read about it they say that I should use multiple sigmoid units on the last layer with binary cross entropy loss function.. actually I didn't understand why this would work and is there a better way to do this?
    Keith Aumiller
    @keithaumiller
    Hey Guys
    I'm availabe for at least the next hour to help out with whatever.
    And if any of you know an easy way to parallelize my R scripts I'd love to hear it. ;)
    Keith Aumiller
    @keithaumiller
    @rawan_la_twitter I haven't done a multi labeled data classification with CNN, but I have done it with neural nets in general.
    first step is to change your label data into a binary set
    once you get the data out of a one field with multiple values and into multiple fields with binary values it's much easier
    one sec let me see if I can find an example.
    Like this:
    Is the R way
    Keith Aumiller
    @keithaumiller
    If you are doing image recognition, that isn't really my bag
    Yogesh Narayan Singh
    @yogids
    @keithaumiller hey buddy...so sorry could not make up for last 2 weeks.. have been travelling and is so hectic at weekends now... hope will be able to make it up from next week..
    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?