{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"9d7cefea-be97-1c40-9d94-0636846ac0c0"},"source":"The goal of this notebook is to learn about competiting on Kaggle and to learn how to train a deep neural network. "},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"07592061-f12d-9424-b560-61da1da606e0"},"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\"]).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":"33c9c20b-66ab-6d98-500b-5601be28980e"},"outputs":[],"source":"#load data \n#Split data into training/validation and test set  \n#Run a linear model \n#Run a two-layer neural network. \nfrom theano.sandbox import cuda\ncuda.use('gpu1')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"545fc2f9-17f7-ff75-ff95-f4203364f3db"},"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":"3.5.2"}},"nbformat":4,"nbformat_minor":0}