{"id":294,"date":"2016-08-04T16:37:54","date_gmt":"2016-08-04T16:37:54","guid":{"rendered":"http:\/\/deberker.com\/archy\/?p=294"},"modified":"2021-11-06T16:01:30","modified_gmt":"2021-11-06T16:01:30","slug":"deep-learning-practical-2-decoding-mnist","status":"publish","type":"post","link":"https:\/\/deberker.com\/archy\/deep-learning-practical-2-decoding-mnist\/","title":{"rendered":"Deep Learning Practical 2: Decoding MNIST"},"content":{"rendered":"

MNIST (Mixed\u00a0National Institute of Standards and Technology) is a database of handwritten digits. Compiled by Yann LeCun and colleagues<\/a>, it’s a classic benchmark problem in machine learning. Pleasingly, you can write a model in Tensorflow that does a decent job of decoding the digits.<\/p>\n

Having got to grips with Python and some of the Tensorflow fundamentals, we cannibalised the official\u00a0Tensorflow tutorial 3 (‘MNIST from scratch’) to make an easy-to-follow tutorial for cracking MNIST using a simple feedforward network. If this is your first time with TF, you might find the Tensorflow Tutorial 1 a better starting point.<\/a><\/p>\n

We go through the following steps:<\/p>\n

    \n
  1. \u00a0Import the data<\/li>\n
  2. \u00a0Look at the data<\/li>\n
  3. \u00a0Figure out normalization<\/li>\n
  4. \u00a0Compile training, validation, and test sets<\/li>\n
  5. \u00a0Define model in Tensorflow<\/li>\n
  6. \u00a0Run the model without training<\/li>\n
  7. \u00a0Train and test<\/li>\n
  8. \u00a0Evaluate performance<\/li>\n<\/ol>\n

    Download the iPython notebook\u00a0here.<\/a><\/p>\n

    Any questions or corrections, give me a shout.<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

    MNIST (Mixed\u00a0National Institute of Standards and Technology) is a database of handwritten digits. Compiled by Yann LeCun and colleagues, it’s a classic benchmark problem in machine learning. Pleasingly, you can write a model in Tensorflow that does a decent job of decoding the digits. Having got to grips with Python and some of the Tensorflow […]<\/p>\n","protected":false},"author":1,"featured_media":293,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"hide_page_title":"","_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[6],"tags":[],"class_list":["post-294","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-neural-networks"],"yoast_head":"\nDeep Learning Practical 2: Decoding MNIST - Archy de Berker<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/deberker.com\/archy\/deep-learning-practical-2-decoding-mnist\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep Learning Practical 2: Decoding MNIST - Archy de Berker\" \/>\n<meta property=\"og:description\" content=\"MNIST (Mixed\u00a0National Institute of Standards and Technology) is a database of handwritten digits. Compiled by Yann LeCun and colleagues, it’s a classic benchmark problem in machine learning. Pleasingly, you can write a model in Tensorflow that does a decent job of decoding the digits. 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