{"id":495928,"date":"2023-06-09T12:20:59","date_gmt":"2023-06-09T10:20:59","guid":{"rendered":"https:\/\/www.scribbr.nl\/?p=495928"},"modified":"2023-08-04T17:37:05","modified_gmt":"2023-08-04T15:37:05","slug":"deep-learning","status":"publish","type":"post","link":"https:\/\/www.scribbr.com\/ai-tools\/deep-learning\/","title":{"rendered":"What Is Deep Learning? | A Beginner’s Guide"},"content":{"rendered":"
Deep learning<\/strong> is a type of technology that allows computers to simulate how our brains work.<\/p>\n More specifically, it is a method that teaches computers to learn and make decisions independently, without explicitly programming them. Instead of telling a computer exactly what to look for, we show it many examples and let it learn on its own.<\/p>\n By showing a computer lots of photos of German Shepherds, you can train it to analyze these images and learn to recognize German Shepherds on its own. With enough training on different breeds, when you show it a new picture, it can make an educated guess about which dog breed it is.<\/figure>\n Deep learning is the technology behind many popular AI applications like chatbots (e.g., ChatGPT<\/a>), virtual assistants, and self-driving cars.<\/p>\n <\/p>\n Deep learning uses artificial neural networks <\/strong>that mimic the structure of the human brain. Similar to the interconnected neurons in our brain, which send and receive information, neural networks form (virtual) layers that work together inside a computer.<\/p>\n These networks consist of multiple layers of nodes, also known as neurons. Each neuron receives input from the previous layer, processes it, and passes it on to the next layer. In this way, the model gradually learns to recognize increasingly complex patterns in the data.<\/p>\n The adjective “deep” in “deep learning” refers to the use of multiple layers in the network through which the data is processed.<\/p>\n There are different types of neural networks, but in its simplest form, a deep learning neural network contains:<\/p>\n It is also possible to train a deep learning model to move backwards, from output to input. This process allows the model to calculate errors and make adjustments so that the next predictions or other outputs are more accurate.<\/p>\n Deep learning is a specialized form of machine learning that was developed to make machine learning more efficient. Essentially, deep learning is an evolution of machine learning.<\/p>\n Machine learning<\/a> (ML) <\/strong>is a subset of artificial intelligence (AI)<\/strong>, the branch of computer science in which machines are taught to perform tasks normally associated with human intelligence, such as decision-making and language-based interaction.<\/p>\n ML is the development of computer programs that can access data and use it to learn for themselves. However, deep learning and ML differ in terms of:<\/p>\n Traditional ML requires structured, labeled data (e.g., quantitative data in the form of numbers and values). Human experts manually identify relevant features from the data and design algorithms<\/strong> (i.e., a set of step-by-step instructions) for the computer to process those features. ML is more dependent on human intervention to learn.<\/p>\n On the other hand, deep learning models can process unstructured data such as audio files or social media posts, and determine which features distinguish different categories of data from one another, without human intervention. In other words, a deep learning network just needs data and a task description, and it learns how to perform its task automatically.<\/p>\nHow does deep learning work?<\/h2>\n
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Deep learning vs. machine learning<\/h2>\n
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