Load the libraries for day 1 and day 2, and pre-fetch the CIFAR10 dataset (may take some time to download, but you will not have to repeat this process)
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## Libraries for day 1
import scipy
import numpy as np
import pandas as pd
import plotly.plotly as py
%run day1/visplots
from plotly.graph_objs import *
from plotly.offline import download_plotlyjs, init_notebook_mode, iplot
from sklearn import preprocessing, metrics
from sklearn.cross_validation import train_test_split, cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.grid_search import GridSearchCV, RandomizedSearchCV
from scipy.stats.distributions import randint
init_notebook_mode()
## Neural Networks
import matplotlib.pyplot as plt
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.optimizers import SGD, RMSprop
from keras.utils import np_utils
## Deep Learning
import os
import cv2
import theano
from scipy import misc
from keras.models import Sequential
from keras.layers.core import Flatten, Dense, Dropout
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.convolutional import ZeroPadding2D
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import np_utils
from keras.datasets import cifar10
x = cifar10.load_data()
print("libraries all imported, ready to go")
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