/soy/ - Soyjaks

ropemax

first

we're giving this thread another 100 reppeys

I wish my gf would let me wear her dresses and skirts

>>15392495
Does your gf need an obese blak bvll?

drag xueen

Subhuman

Demigod

Tsmt!

>>15392535
Eat my chainsaw, bitch! or something

I can't wait for my top to arrive to complete my first femboy outfit.
The wait is fucking driving me insane!

>>15392528
ima join :3

POSTED IT AGAIN AWARD I SAW THIS EXACT BAIT POST 1 WEEK AGO

>>15392603
Sentient

>>15392495
Mine paints my nails sometimes

why is she crying tho

>>15392685
cause he will never be a real woman

>>15392528
Lets all join this wholesome community besties!

>>15392316 (OP)
+=!!THIS IS BAIT STOP REPLYING YOU FUCKING NIGGERRRS!!=+

>>15392691
How sad!

>>15392662
Acktschually, I am a euromutt, not an amerimutt.

>>15392712
nigga 90% of these reppeys are samefagged

same tbh

DERAIL

The post above and below are gay

RERAIL

I just drank my cats saliva.

Gemmy getting buried underneath all that bait and goonslop threads.
This is sad.

posted it again award

Just another Chud in the Life of Jimmy Nutren

hey guys it’s me noounrdy nooumly, and this baited me

>>15393513
'gated o algo

We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 cortical rat neurons
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 cortical rat neurons
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 cortical rat neurons
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 cortical rat neurons
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 cortical rat neurons
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 cortical rat neurons
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 cortical rat neurons
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 cortical rat neurons
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 cortical rat neurons
We trained a large, deep convolutional neural network to classify the 1.2 million
high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5%
and 17.0% which is considerably better than the previous state-of-the-art. The
neural network, which has 60 million parameters and 650,000 cortical rat neurons