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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

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

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?

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. ;)

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

Also for n categorical to n binary columns... are we trying to make dummy variables here?

I ended up just using Fork instead of trying to do some complicated multithreading stuff.

;)

I'm going to fire up a cyclops.io video chat

if anybody cares to join me

like what are the steps

or so /

?

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:

I was trying to make a program for image compression using k means clustering

Can someone tell me whatâ€™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()

This is the algorithm which I have implemented using Java.