{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"dcaf15f2-8e01-bb9f-1ba0-85adb2f6385e"},"source":"In 2013, we hosted one of our favorite for-fun competitions:  Dogs vs. Cats. Much has since changed in the machine learning landscape, particularly in deep learning and image analysis. Back then, a tensor flow was the diffusion of the creamer in a bored mathematician's cup of coffee. Now, even the cucumber farmers are neural netting their way to a bounty.\n\nMuch has changed at Kaggle as well. Our online coding environment Kernels didn't exist in 2013, and so it was that we approached sharing by scratching primitive glpyhs on cave walls with sticks and sharp objects. No more. Now, Kernels have taken over as the way to share code on Kaggle. IPython is out and Jupyter Notebook is in. We even have TensorFlow. What more could a data scientist ask for? But seriously, what more? Pull requests welcome.\n\nWoof Meow\n\nWe are excited to bring back the infamous Dogs vs. Cats classification problem as a playground competition with kernels enabled. Although modern techniques may make light of this once-difficult problem, it is through practice of new techniques on old datasets that we will make light of machine learning's future challenges.\n\n#1st, lets check the content of the folder..."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"91124f61-11f8-7bee-8791-88bdc86a255b"},"outputs":[],"source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input/train\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"1f3d0925-9605-ddeb-bfde-5efde0e4e74e"},"outputs":[],"source":"from matplotlib.pyplot import imshow\nfrom PIL import Image\n\npil_im = Image.open('../input/train/cat.0.jpg', 'r')\nimshow(np.asarray(pil_im))"},{"cell_type":"markdown","metadata":{"_cell_guid":"663d2ab3-471e-b142-8825-09d5a2078e69"},"source":"Aww!!... cute kitty..."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ec49be31-45d4-748e-7b18-d39c6173cef6"},"outputs":[],"source":"import tensorflow as tf"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ccf1ee23-0bd5-1b08-a897-1f7ef94b94c6"},"outputs":[],"source":""}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.0"}},"nbformat":4,"nbformat_minor":0}