{"id":11156,"date":"2018-08-27T14:57:23","date_gmt":"2018-08-27T09:27:23","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=11156"},"modified":"2018-09-11T21:36:48","modified_gmt":"2018-09-11T16:06:48","slug":"deep-learning-and-neural-networks-simplified-part-1","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/deep-learning-and-neural-networks-simplified-part-1\/","title":{"rendered":"Deep Learning and Neural Networks \u2013 Simplified (Part 1)"},"content":{"rendered":"
\n

The entire field of artificial intelligence, in the last few years, is built upon deep learning or deep neural networks. Notably, Apple’s Siri, Google-DeepMinds’ AlphaGo, or the self-driving mechanism in Tesla cars are all based on deep learning. Here, my goal is to make deep learning neural networks\u00a0much more accessible for everyone. In this series of articles, you will gain an intuitive\u00a0understanding of the logic and mathematics behind deep learning. Moreover, you will also learn how to practice it by building a few models from scratch. In almost every book on the topic, deep learning is compared to the complicated biological neural networks in the human brain. I, however, think Super Mario Bros. can provide more intuitive yet friendly insights into deep learning than neurosurgeons.<\/p>\n

\"\"<\/a>

Deep Learning Neural Networks and Super Mario Bros – by Roopam<\/p><\/div>\n

Deep Learning and Plumbing<\/span><\/h2>\n

I live in an eighteen-floor high-rise building in Mumbai. With close to hundred flats in the edifice, one would assume the water pipelines to form quite a mesh. A few years ago we were having a problem of high water pressure in our bathroom. I asked our building plumber to check the pipelines and fixed the water pressure. He climbed up the duct to twiddle around with the numerous knobs available to manage the flow of water. I stayed in the bathroom and opened the tap to monitor the water flow.<\/p>\n

The plumber was using this ingenious method to fix the problem called “hit or miss”. He rotated a few knobs and shouted: “is the water pressure reducing?”. I, in response, shouted back “no it’s the same.” This process went on for a while. After some time, he gave up. In this process, without fixing our problem he must have screwed up the water pressure for at least a few of our neighbors as well. Since then we have decided to treat our bath shower and taps as acupressure devices.<\/p>\n

This, in reality, was a job for Super Mario Bros – the beloved plumbers from the famous Nintendo video game from the eighties. Let’s see, how they would have solved the problem using the deep learning methods.<\/p>\n

Water Pipelines and Deep Learning Neural Networks<\/span><\/h2>\n

Let’s create a much simpler layout of pipelines to understand the problem of water pressure in my bathroom. In the process, we will learn how deep learning works. Imagine there are two water tanks through which the water is supplied in the building. These tanks are connected by pipelines with an additional storage space with multiple chambers in the middle. I will come back to these middle chambers aka the hidden nodes and layers in the neural networks. Note, these hidden layers have the special non-linear properties. I have displayed this non-linear behavior like the turbulence or whirl in these hidden and output chambers (nodes).<\/p>\n

\"\"<\/a><\/p>\n

 <\/p>\n

The flow of water or water pressure from the tab is managed by the six circular red knobs. Now, essentially, the entire problem of management of water pressure is restricted to the optimal settings of these six knobs. Here, the yellow and black input tanks towards the left are the input variables. The water coming out of the tap towards the right is the output variable.<\/p>\n

For example, input variables could be education and years of experience of a person. The output tap here is the salary of the person. Now, if we are aware of the right settings for the six knobs we will get the right output (estimation) for salary with the input values. Before we learn the logic and mathematics to identify the right settings for the red knobs, let’s go back to the non-linearity of hidden and output layers.<\/p>\n\n\n\n
\n

Quick Note<\/span><\/h4>\n

Deep neural networks used for deep learning have more than one hidden layer. The overall logic and mathematics don’t change much with the addition of layers. Deep learning is extremely useful for machines to derive meaning from alternate data sources like images, videos, language, and text. To decipher these alternate data, additional layers and components are added to the network. These additional layers are no different than additional water filters and pressure boosters\/regulators in our water pipeline example. These networks with additional components are referred with fancy names like convolutional neural networks (CNN), and recurrent neural networks (RNN).<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Non-Linear Hidden Layers & Sprinklers\"\"<\/a><\/span><\/h2>\n

Let’s try to understand non-linearity through the garden sprinkler in the adjacent image. The idea with the sprinkler is to provide water to each and every part of the lawn. Now, if you linearly throw water in one direction then you will end up making a large water puddle in one corner while the rest of the lawn is still dry. An ingenious yet simple solution to this problem is to add a lever near the edge of the sprinkler. This lever will oscillate because of the flow of the water and make the water spread non-linearly across the lawn.<\/p>\n

Incidentally, non-linearity turned out to be quite a powerful tool for highly accurate estimations and predictions. Every physicist will agree that non-linearity is nature’s best-kept secret to create this beautiful universe. All fluids are capable of displaying non-linear behavior. You would agree if you have ever experienced mid-air turbulence while flying.<\/p>\n

Neural networks try to infuse non-linearity by adding similar sprinkler-like levers in the hidden layers. This often results in an identification of better relationships between input variables (for example education) and output (salary). It kind of makes sense since if you stay in school for eternity it won’t improve your earnings infinitely.\u00a0These levers produce the whirl or turbulence in the hidden and output nodes of the neural networks. These levers and resulting non-linearity are responsible for the high accuracy neural networks display for predictions. The linear models just couldn’t unfathomed the meaning within the complicated images or textual datasets.<\/p>\n

Levers and Activation Function for\u00a0<\/span>Non-Linearity<\/span><\/h2>\n

How do neural networks make the data follow the sprinkler-like behavior? They use non-linear activation functions in the hidden and output nodes. These activation functions, like levers, come in many shapes and sizes. I will, however, just talk about the most popular activation functions. The most simple activation function is the binary step function. This is like an on\/off electric switch. It is off (zero) for x less than 0, and on (one) for the values of x greater than or equal to 0.\"\"<\/a><\/p>\n

The classic lever of activation functions is sigmoid or logit function. Instead of on\/off switch it behaves like the regulator of fan or light. It smoothly moves from off (0) to on(1). We will use the sigmoid function when we will do our maths for neural networks in the next part of this series.<\/p>\n

\"\"<\/a><\/p>\n

Finally, there are two other useful variants of activation function called ReLU and TanH. As you would notice the former is similar to the step function and the latter to the sigmoid function.\"\"<\/a><\/p>\n

Backpropagation to Find the Optimal Settings of Water Knobs<\/span><\/h2>\n

\"\"<\/p>\n

Now, since you know all the important components of the neural networks pipeline, let go back to the main objective of finding the optimal settings for the water knobs. Once you know the optimal setting of the knobs for your data, you have essentially developed the deep learning model.<\/p>\n

Remember the way I was shouting back at my buildings plumber to tell him about the water pressure while he was working with the knobs in the duct. Neural networks use a similar mechanism to identify the optimal settings of knobs or weights for the network. This method is called backpropagation. Each time the neural network plumber changes the weights, an algorithm (similar to me) shouts back about the state of the output given the inputs.<\/p>\n

Unlike my building’s plumber, the neural networks’ plumber, let’s call him Super Mario,\u00a0 learns from the shout back. Each shout back helps Super Mario identify better settings for the knobs. In this iterative manner, Mario identifies the optimal or the best setting for knobs to manage the right water pressure for your house without screwing up the water pressure for the neighbors. Sometimes, Mario uses additional knobs, or water filters or pressure regulators\/boosters to find the best setting like the convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Incidentally, CNNs\u00a0are highly useful for machines to understand image datasets. Moreover, RNNs\u00a0are equally useful for machines to derive meaning out of human languages and sequential data.<\/p>\n

Sign-off Note<\/span><\/h4>\n

I think neural networks and deep learning are made additionally complicated by comparing them to the most sophisticated and advanced object in the known universe i.e. the human brain. The biological neural networks in the brain are developed over millions of years using the process called evolution. Whereas, artificial neural networks (ANN) and deep learning are just a few decades old. I think we can be better off treating ANN as a plumbing job.<\/p>\n

In the next parts of this series, be prepared to learn the mathematics of neural networks – trust me it will be fun with Super Mario at our rescue. Moreover, after the theory, you will do some practicals with the plumbing job. Be prepared to get wet since no plumber is allowed to leave the building dry.<\/p>\n","protected":false},"excerpt":{"rendered":"

The entire field of artificial intelligence, in the last few years, is built upon deep learning or deep neural networks. Notably, Apple’s Siri, Google-DeepMinds’ AlphaGo, or the self-driving mechanism in Tesla cars are all based on deep learning. Here, my goal is to make deep learning neural networks\u00a0much more accessible for everyone. In this series<\/p>\n

Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":11158,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[86],"tags":[],"jetpack_publicize_connections":[],"yoast_head":"\nDeep Learning and Neural Networks \u2013 Simplified (Part 1) – YOU CANalytics |<\/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:\/\/ucanalytics.com\/blogs\/deep-learning-and-neural-networks-simplified-part-1\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Deep Learning and Neural Networks \u2013 Simplified (Part 1) – YOU CANalytics |\" \/>\n<meta property=\"og:description\" content=\"The entire field of artificial intelligence, in the last few years, is built upon deep learning or deep neural networks. 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Here I will share a few cool learning resources for these topics. These resources include documentaries, TED talks, online lecture videos, and books. There are several videos\u2026","rel":"","context":"In "Analytics Book Club"","block_context":{"text":"Analytics Book Club","link":"https:\/\/ucanalytics.com\/blogs\/category\/analytics-book-club\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2015\/11\/Alice-in-Wonderland-Copy.jpg?fit=433%2C274&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]},{"id":11290,"url":"https:\/\/ucanalytics.com\/blogs\/math-of-deep-learning-neural-networks-simplified-part-2\/","url_meta":{"origin":11156,"position":1},"title":"Math of Deep Learning Neural Networks – Simplified (Part 2)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Welcome back to this series of articles on deep learning and neural networks. In the last part, you learned how training a\u00a0deep learning network is similar to a plumbing job. This time you will learn the math of deep learning. We will continue to use the plumbing analogy to simplify\u2026","rel":"","context":"In "Deep Learning Neural Networks"","block_context":{"text":"Deep Learning Neural Networks","link":"https:\/\/ucanalytics.com\/blogs\/category\/deep-learning-neural-networks\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/08\/Deep-Learning-Neural-Networks-and-Plumbing-Job.jpg?fit=796%2C597&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/08\/Deep-Learning-Neural-Networks-and-Plumbing-Job.jpg?fit=796%2C597&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/08\/Deep-Learning-Neural-Networks-and-Plumbing-Job.jpg?fit=796%2C597&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/08\/Deep-Learning-Neural-Networks-and-Plumbing-Job.jpg?fit=796%2C597&ssl=1&resize=700%2C400 2x"},"classes":[]},{"id":4054,"url":"https:\/\/ucanalytics.com\/blogs\/artificial-neural-networks-retail-case-study-example-part-8\/","url_meta":{"origin":11156,"position":2},"title":"Artificial Neural Networks, Made Easy – Retail Case Study Example (Part 8)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Welcome back to\u00a0our retail case study example\u00a0for campaign and marketing analytics. So far in this case study, we were working on a\u00a0classification problem to identify customers with a higher likelihood to purchase products from\u00a0the campaign catalogues. In the last article on\u00a0model selection, we noticed that artificial neural networks performed better\u2026","rel":"","context":"In "Marketing Analytics"","block_context":{"text":"Marketing Analytics","link":"https:\/\/ucanalytics.com\/blogs\/category\/marketing-analytics\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/09\/Artificial-Neural-Networks.jpg?fit=346%2C336&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]},{"id":11578,"url":"https:\/\/ucanalytics.com\/blogs\/deep-learning-models-simplified-part-3\/","url_meta":{"origin":11156,"position":3},"title":"Deep Learning Models Simplified (Part 3)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Facebook was a major sensation and a source of great amusement in a British\u00a0country house in the early 20th century. It was such a big hit that it got a special mention in a newspaper published in the year 1902. Facebook, then, of course, had a completely different meaning than\u2026","rel":"","context":"In "Deep Learning Neural Networks"","block_context":{"text":"Deep Learning Neural Networks","link":"https:\/\/ucanalytics.com\/blogs\/category\/deep-learning-neural-networks\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/10\/Popeye-Deep-Learning.jpg?fit=960%2C686&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/10\/Popeye-Deep-Learning.jpg?fit=960%2C686&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/10\/Popeye-Deep-Learning.jpg?fit=960%2C686&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/10\/Popeye-Deep-Learning.jpg?fit=960%2C686&ssl=1&resize=700%2C400 2x"},"classes":[]},{"id":11583,"url":"https:\/\/ucanalytics.com\/blogs\/convolutional-neural-networks-cnn-simplified-part-4\/","url_meta":{"origin":11156,"position":4},"title":"Convolutional Neural Networks (CNN) Simplified (Part 4)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Welcome back to the deep learning example to build an OCR application. The idea of this simple application is to identify numbers in an image of written text. In the last part, we used three different models and got the following accuracy for identification of the test images: Model 1\u2026","rel":"","context":"In "Deep Learning Neural Networks"","block_context":{"text":"Deep Learning Neural Networks","link":"https:\/\/ucanalytics.com\/blogs\/category\/deep-learning-neural-networks\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/10\/Convolutional-Neural-Networks-CNN-Filters-Polaroid-Glasses-by-Roopam.jpg?fit=775%2C581&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/10\/Convolutional-Neural-Networks-CNN-Filters-Polaroid-Glasses-by-Roopam.jpg?fit=775%2C581&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/10\/Convolutional-Neural-Networks-CNN-Filters-Polaroid-Glasses-by-Roopam.jpg?fit=775%2C581&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2018\/10\/Convolutional-Neural-Networks-CNN-Filters-Polaroid-Glasses-by-Roopam.jpg?fit=775%2C581&ssl=1&resize=700%2C400 2x"},"classes":[]},{"id":3973,"url":"https:\/\/ucanalytics.com\/blogs\/model-selection-retail-case-study-example-part-7\/","url_meta":{"origin":11156,"position":5},"title":"Model Selection – Retail Case Study Example (Part 7)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Model Selection This is a continuation of our retail case study example for campaign and marketing analytics. In the previous two parts, we discussed a couple of decision tree algorithms (CART and C4.5)\u00a0for classification. Recall a previous case study example on\u00a0banking and risk management where we discussed logistic regression\u00a0which is\u2026","rel":"","context":"In "Marketing Analytics"","block_context":{"text":"Marketing Analytics","link":"https:\/\/ucanalytics.com\/blogs\/category\/marketing-analytics\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/09\/photo.jpg?fit=1200%2C1029&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/09\/photo.jpg?fit=1200%2C1029&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/09\/photo.jpg?fit=1200%2C1029&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/09\/photo.jpg?fit=1200%2C1029&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/09\/photo.jpg?fit=1200%2C1029&ssl=1&resize=1050%2C600 3x"},"classes":[]}],"_links":{"self":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/11156"}],"collection":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/comments?post=11156"}],"version-history":[{"count":0,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/11156\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media\/11158"}],"wp:attachment":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media?parent=11156"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/categories?post=11156"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/tags?post=11156"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}