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vvanirudh /
Created Feb 6, 2018
Random fourier features using both sines and cosines embedding for Gaussian kernel
from sklearn.base import BaseEstimator
from sklearn.exceptions import NotFittedError
import numpy as np
class IRFF(BaseEstimator):
Random fourier features using the improved embedding
j314erre /
Created Jul 13, 2016
load pre-trained word2vec into cnn-text-classification-tf
import tensorflow as tf
import numpy as np
class TextCNN(object):
A CNN for text classification.
Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
def __init__(
Internet Engineering Task Force (IETF)                          M. Jones
Request for Comments: 6750                                     Microsoft
Category: Standards Track                                       D. Hardt
ISSN: 2070-1721                                              Independent
                                                            October 2012

The OAuth 2.0 Authorization Framework: Bearer Token Usage



This is unmaintained, please visit Ben-PH/spacemacs-cheatsheet

Useful Spacemacs commands

  • SPC q q - quit
  • SPC w / - split window vertically
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View gist:5170087
import numpy as np
from scipy import linalg
from sklearn.utils import array2d, as_float_array
from sklearn.base import TransformerMixin, BaseEstimator
class ZCA(BaseEstimator, TransformerMixin):
def __init__(self, regularization=10**-5, copy=False):
self.regularization = regularization
Mistobaan /
Last active Aug 26, 2021
Tensorflow Internals Debugging Techniques

Machine Setup August 2016

Linux Ubuntu 2016.

  • 1080 GTX
  • SDK 8.0
  • CuDNN 5.1

ENABLE Core dumps

ulimit -c unlimited
View tmux_cheatsheet.markdown

tmux cheatsheet

As configured in my dotfiles.

start new:


start new with session name:

karpathy /
Last active Oct 23, 2021
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
import numpy as np
# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)