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Motivation: Deep learning in vision, speech, text, robotics.
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Deep learning courses:
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from functools import singledispatch | |
class Document: | |
pass | |
class Paragraph(Document): | |
def __init__(self, text): | |
self.text = text |
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{-# LANGUAGE TypeOperators #-} | |
{-# LANGUAGE GeneralizedNewtypeDeriving #-} | |
import Control.Applicative | |
import Data.Char | |
import Data.List (intersperse) | |
import Data.Monoid hiding (All, Any) | |
import Data.Foldable hiding (all, any) | |
import Prelude hiding (all, any) |
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from typing import ( | |
List, | |
TypeVar, | |
) | |
# Type aliases | |
A = TypeVar('A') | |
Digit = int | |
Matrix = List[List[A]] | |
Grid = Matrix[Digit] |
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/** | |
* Created by eurus on 28/02/2017. | |
*/ | |
package components | |
import collection.immutable.Queue | |
import ActionType._ | |
sealed trait SignalValue | |
object `0` extends SignalValue { |
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import logging | |
from gensim.models.doc2vec import ( | |
Doc2Vec, | |
TaggedDocument, | |
) | |
logging.basicConfig( | |
format='%(asctime)s : %(threadName)s : %(levelname)s : %(message)s', | |
level=logging.DEBUG, |
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def descriptors_to_sufficient_statistics(xx, gmm, **kwargs): | |
# yael assumes that the data is in C-contiguous format. | |
xx = np.ascontiguousarray(np.atleast_2d(xx)) | |
N = xx.shape[0] | |
K = gmm.k | |
D = gmm.d | |
# Compute posterior probabilities using yael. | |
Q = gmm_predict_proba(xx, gmm) # NxK |
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class Expr(object): | |
def accept(self, visitor): | |
method_name = 'visit_{}'.format(self.__class__.__name__.lower()) | |
visit = getattr(visitor, method_name) | |
return visit(self) | |
class Int(Expr): | |
def __init__(self, value): |
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class Expr(object): | |
def accept(self, visitor): | |
method_name = 'visit_{}'.format(self.__class__.__name__.lower()) | |
visit = getattr(visitor, method_name) | |
return visit(self) | |
class Int(Expr): | |
def __init__(self, value): | |
self.value = value |
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import numpy as np | |
def fvecs_read(filename, c_contiguous=True): | |
fv = np.fromfile(filename, dtype=np.float32) | |
if fv.size == 0: | |
return np.zeros((0, 0)) | |
dim = fv.view(np.int32)[0] | |
assert dim > 0 | |
fv = fv.reshape(-1, 1 + dim) |