{"id":4587,"date":"2022-11-10T17:48:17","date_gmt":"2022-11-11T01:48:17","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=4587"},"modified":"2025-04-24T17:16:36","modified_gmt":"2025-04-24T17:16:36","slug":"deep-learning-how-it-works","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/deep-learning-how-it-works\/","title":{"rendered":"Deep Learning: How it Works"},"content":{"rendered":"\n
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Photo by JJ Ying<\/a> on Unsplash<\/a><\/p>\n\n\n\n

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Our lives have transitioned to revolve around Artificial Intelligence (AI) and Machine Learning (ML). Everybody is talking about it and it\u2019s been implemented in our day-to-day tasks and actions without us even realizing sometimes.<\/p>\n

They are the hottest topics right now, and everybody wants to know more. People throw the term \u201cAI\u201d around, from developers, companies, and even people who have no understanding; but are living in a tech-driven world.<\/p>\n

Deep Learning<\/h1>\n

Deep Learning<\/strong> is a Machine Learning method that teaches computers to do what comes naturally to humans. It trains an algorithm to predict outputs, given a set of inputs. Supervised and Unsupervised Learning can be used with Deep Learning.<\/p>\n

The hype about Deep Learning is due to the fact that Deep learning models have been able to achieve higher levels of recognition accuracy than ever before. Recent advances in deep learning have exceeded human-level performance in tasks such as image recognition.<\/p>\n

So how does this method work that\u2019s out-performing human-level?<\/h1>\n

The majority of Deep Learning methods use neural network architectures. You may hear Deep Learning is referred to as Deep Neural Networks sometimes. The term \u2018Deep\u2019 relates to the number of hidden layers in the neural network.<\/p>\n

Neural Network<\/h1>\n

A Neural Network<\/strong> is a network of biological neurons. In the use of AI, Artificial Neural Network contains artificial neurons or nodes.<\/p>\n

If we refer back to the definition of AI: the ability of a computer or a computer-controlled robot to perform tasks that are usually done by humans as they require human intelligence. <\/em>We can connect the dots of a Neural Network being a structure of biological neurons, similar to the human brain.<\/p>\n

So let\u2019s dive into the brain of an AI.<\/p>\n

Artificial Neural Networks<\/strong> (ANNs) are made up of neurons that contain three different layers: an input layer, one or more hidden layers, and an output layer. Each neuron is connected to another neuron and is where computation happens.<\/p>\n

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  1. Input Layer<\/strong> \u2014 receives the input data<\/li>\n
  2. Hidden Layer(s)<\/strong> \u2014 perform mathematical computations on the input data<\/li>\n
  3. Output Layer<\/strong> \u2014 returns the output data.<\/li>\n<\/ol>\n

    The term \u201cDeep\u201d in Deep Learning refers to more than one hidden layer.<\/p>\n

    Perceptrons<\/h2>\n

    Perceptrons were introduced by Frank Rosenblatt in 1957. There are two types of Perceptrons:<\/p>\n