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The natural way to start with CUDA

The natural way to start with CUDA if you plan to be self taught is:

1. CUDA by Example: An Introduction to General-Purpose GPU Programming by Jason Sanders and Edward Kandrot Nice introduction. It is more like playing with your GPU and admire its capabilities.

2. Programming Massively Parallel Processors, Second Edition: A Hands-on Approach by David B. Kirk and Wen-mei W. Hwu It explains a lot of things in GPU Programming. You simply can't go without it.

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diff --git a/lib/face_filter.py b/lib/face_filter.py
index 096709d..cb4a10f 100644
--- a/lib/face_filter.py
+++ b/lib/face_filter.py
@@ -5,7 +5,7 @@ import logging
 
 from lib.vgg_face import VGGFace
 from lib.image import read_image
-from plugins.extract.pipeline import Extractor
+from plugins.extract.pipeline import Extractor, ExtractMedia

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extern "C" int runOpenCV(halide_buffer_t *im1, halide_buffer_t *im2, halide_buffer_t *im3, halide_buffer_t *out) {
    // width
    const int min0 = out->dim[0].min;
    const int max0 = out->dim[0].min + out->dim[0].extent - 1;
    const int width = out->dim[0].extent;

    // height
    const int min1 = out->dim[1].min;
    const int max1 = out->dim[1].min + out->dim[1].extent - 1;
    const int height = out->dim[1].extent;

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@InProceedings{Campbell_2017_ICCV,
author = {Campbell, Dylan and Petersson, Lars and Kneip, Laurent and Li, Hongdong},
title = {Globally-Optimal Inlier Set Maximisation for Simultaneous Camera Pose and Feature Correspondence},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}
@InProceedings{Huang_2017_ICCV,
author = {Huang, Chao-Tsung},
title = {Robust Pseudo Random Fields for Light-Field Stereo Matching},

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# In[]

import SimpleITK as sitk
import vtk
import numpy as np
import scipy as sp
from scipy.stats.mstats import mquantiles
import sys

from vtk.util.vtkConstants import *

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# In[]
import gym
import numpy as np
np.set_printoptions(precision=3, suppress=True)

# In[]

def p_pp(policy):
   mapping = {
      0: '<',

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# In[]
import gym
import numpy as np
import theano
import theano.tensor as T
import lasagne
import sklearn.preprocessing
np.set_printoptions(precision=2)

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# In[]

import collections
import gym
import numpy as np
import math


# In[]
class DiscretePolicy(object):

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# In[]
class DiscretePolicy(object):
    def __init__(self, env):
        if not issubclass(type(env), gym.envs.toy_text.discrete.DiscreteEnv):
            raise Exception('env should be subclass of gym.envs.toy_text.'
                            'discrete.DiscreteEnv')
        self.env = env
        self.policy = np.array([env.action_space.sample() for i in
                                range(self.env.nS)],
                               dtype=int)

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# In[]

import collections
import gym
import numpy as np
import math

# In[]

class IterationAgent(object):