{"id":1064,"date":"2016-12-11T22:38:11","date_gmt":"2016-12-11T19:38:11","guid":{"rendered":"http:\/\/artificialbrain.xyz\/?p=1064"},"modified":"2021-08-03T15:49:59","modified_gmt":"2021-08-03T12:49:59","slug":"machine-learning-recipes-with-josh-gordon","status":"publish","type":"post","link":"https:\/\/www.newworldai.com\/machine-learning-recipes-with-josh-gordon\/","title":{"rendered":"Machine Learning Recipes with Josh Gordon"},"content":{"rendered":"<p style=\"text-align: justify;\"><span style=\"color: #ff0000; font-family: 'Open Sans';\"><strong>Hello World &#8211; Machine Learning Recipes #1<\/strong><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: 'Open Sans';\">Six lines of Python is all it takes to write your first machine learning program! In this episode, we&#8217;ll briefly introduce what machine learning is and why it&#8217;s important. Then, we&#8217;ll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up.<\/span><\/p>\n<p style=\"text-align: justify;\"><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/cKxRvEZd3Mw?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal\" width=\"760\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p style=\"text-align: justify;\"><span style=\"color: #ff0000; font-family: 'Open Sans';\"><strong>Visualizing a Decision Tree &#8211; Machine Learning Recipes #2<\/strong><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: 'Open Sans';\">Last episode, we treated our Decision Tree as a blackbox. In this episode, we&#8217;ll build one on a real dataset, add code to visualize it, and practice reading it &#8211; so you can see how it works under the hood. And hey &#8212; I may have gone a little fast through some parts. Just let me know, I&#8217;ll slow down. Also: we&#8217;ll do a Q&amp;A episode down the road, so if anything is unclear, just ask!<\/span><\/p>\n<p style=\"text-align: justify;\"><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/tNa99PG8hR8?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal\" width=\"760\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p style=\"text-align: justify;\"><span style=\"color: #ff0000; font-family: 'Open Sans';\"><strong>What Makes a Good Feature? &#8211; Machine Learning Recipes #3<\/strong><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: 'Open Sans';\">Good features are informative, independent, and simple. In this episode, we&#8217;ll introduce these concepts by using a histogram to visualize a feature from a toy dataset. Updates: many thanks for the supportive feedback! I\u2019d love to release these episodes faster, but I\u2019m writing them as we go. That way, I can see what works and (more importantly) where I can improve.<\/span><\/p>\n<p style=\"text-align: justify;\"><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/N9fDIAflCMY?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal\" width=\"760\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p style=\"text-align: justify;\"><span style=\"color: #ff0000; font-family: 'Open Sans';\"><strong>Let\u2019s Write a Pipeline &#8211; Machine Learning Recipes #4<\/strong><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: 'Open Sans';\">In this episode, we\u2019ll write a basic pipeline for supervised learning with just 12 lines of code. Along the way, we&#8217;ll talk about training and testing data. Then, we\u2019ll work on our intuition for what it means to \u201clearn\u201d from data.<\/span><\/p>\n<p style=\"text-align: justify;\"><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/84gqSbLcBFE\" width=\"760\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p style=\"text-align: justify;\"><span style=\"color: #ff0000; font-family: 'Open Sans';\"><strong>Writing Our First Classifier &#8211; Machine Learning Recipes #5<\/strong><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: 'Open Sans';\">Welcome back! It&#8217;s time to write our first classifier. This is a milestone if you\u2019re new to machine learning. We&#8217;ll start with our code from episode #4 and comment out the classifier we imported. Then, we&#8217;ll code up a simple replacement &#8211; using a scrappy version of k-Nearest Neighbors.<\/span><\/p>\n<p style=\"text-align: justify;\"><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/AoeEHqVSNOw\" width=\"760\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p style=\"text-align: justify;\"><span style=\"color: #ff0000; font-family: 'Open Sans';\"><strong>Train an Image Classifier with TensorFlow for Poets &#8211; Machine Learning Recipes #6<\/strong><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: 'Open Sans';\">Monet or Picasso? In this episode, we\u2019ll train our own image classifier, using TensorFlow for Poets. Along the way, I\u2019ll introduce Deep Learning, and add context and background on why the classifier works so well. Here are links to learn more, thanks for watching, and have fun!<\/span><\/p>\n<p style=\"text-align: justify;\"><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/cSKfRcEDGUs?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal\" width=\"760\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n<p style=\"text-align: justify;\"><span style=\"color: #ff0000; font-family: 'Open Sans';\"><strong>Classifying Handwritten Digits with TF.Learn &#8211; Machine Learning Recipes #7<\/strong><\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-family: 'Open Sans';\">Last time we wrote an image classifier using TensorFlow for Poets. This time, we\u2019ll write a basic one using TF.Learn. To make it easier for you to try this out, I wrote a Jupyter Notebook for this episode &#8212; https:\/\/goo.gl\/NNlMNu &#8212; and I\u2019ll start with a quick screencast of installing TensorFlow using Docker, and serving the notebook. This is a great way to get all the dependencies installed and properly configured. I&#8217;ve linked some additional notebooks below you can try out, too. Next, I\u2019ll start introducing a linear classifier. My goal here is just to get us started. I\u2019d like to spend a lot more time on this next episode, if there\u2019s interest? I have a couple alternate ways of introducing them that I think would be helpful (and I put some exceptional links below for you to check out to learn more, esp. Colah&#8217;s blog and CS231n &#8211; wow!). Finally, I\u2019ll show you how to reproduce those nifty images of weights from TensorFlow.org&#8217;s Basic MNIST\u2019s tutorial.<\/span><\/p>\n<p style=\"text-align: justify;\"><iframe loading=\"lazy\" src=\"https:\/\/www.youtube.com\/embed\/Gj0iyo265bc?list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal\" width=\"760\" height=\"315\" frameborder=\"0\" allowfullscreen=\"allowfullscreen\"><\/iframe><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Hello World &#8211; Machine Learning Recipes #1 Six lines of Python is all it takes to write your first machine learning program! In this<\/p>\n","protected":false},"author":2,"featured_media":1069,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1475],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.2.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Machine Learning Recipes with Josh Gordon - New World : Artificial Intelligence<\/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:\/\/www.newworldai.com\/machine-learning-recipes-with-josh-gordon\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning Recipes with Josh Gordon - New World : Artificial Intelligence\" \/>\n<meta property=\"og:description\" content=\"Hello World &#8211; Machine Learning Recipes #1 Six lines of Python is all it takes to write your first machine learning program! 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