{"id":38061,"date":"2021-01-13T10:00:16","date_gmt":"2021-01-13T15:00:16","guid":{"rendered":"https:\/\/simpleprogrammer.com\/?p=38061"},"modified":"2021-01-13T07:12:09","modified_gmt":"2021-01-13T12:12:09","slug":"minimum-guide-deep-learning","status":"publish","type":"post","link":"https:\/\/simpleprogrammer.com\/minimum-guide-deep-learning\/","title":{"rendered":"Deep Learning: The Absolute Minimum an Interested Developer Should Know"},"content":{"rendered":"
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\"minimumIf you are a developer and have been reading articles and posts on deep learning, you must be intrigued by how the industry is progressing.<\/p>\n<\/div>\n

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For those of you who haven\u2019t heard, deep learning is a machine learning<\/a> (ML) technique that teaches systems to perform tasks that come naturally to humans. It is the key technology behind the voice control options in hands-free speakers, TVs, tablets, and smartphones, and it is key to developing driverless cars.<\/p>\n<\/div>\n

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For a developer looking to switch fields or niche, deep learning brings in a lot of opportunities. However, how to get started is still a big question if you want to make a mark in the industry.<\/p>\n<\/div>\n

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In this post, you will learn about the rapid growth of deep learning, why the traditional approach to teaching deep learning will not work for you, and how you can really get started as an interested developer<\/p>\n<\/div>\n

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How has Deep Learning Attained Impressive Results in Such a Short Time Span?<\/h1>\n<\/div>\n
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The shortest answer to this question is \u2018accuracy.\u2019\u00a0Deep learning<\/a>\u00a0executes recognition accuracy at higher levels, helping consumer electronics meet user expectations. Accuracy is essential for safety-critical use cases like driverless automobiles.<\/p>\n<\/div>\n

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With constant technological advancements, deep learning has improved to the point that it is outperforming humans in tasks like categorizing objects in images.<\/p>\n<\/div>\n

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This is what makes it a hot topic among developers. However, its application is quite difficult because it doesn\u2019t include your standard coding. It includes data as well. So, you will be circling between large amounts of labeled data and substantial computing power.<\/p>\n<\/div>\n

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Deep learning applications are changing the course of how we perform certain tasks\u2014from automated driving to medical devices, aerospace and research, and more.<\/p>\n<\/div>\n

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Traditional Answer to \u201cHow to Get Started in Deep Learning?\u201d<\/h1>\n<\/div>\n
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Most developers looking to explore the deep learning niche leave it halfway because it starts with definition and transits to concepts of algorithms and mathematical descriptions, which only increase complexity.<\/p>\n<\/div>\n

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While mathematical descriptions, concepts of algorithms, and definitions are concise, clear, and often straightforward, they are dull, dry, and require a prerequisite mathematical background to parse and interpret.<\/p>\n<\/div>\n

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This is the primary reason why you only get to study machine learning and its stages as a graduate-level subject in colleges and universities. It is because the topic only requires you to have the basic principles cleared. With that said, to start a career in deep learning as a developer, it is advisable that you have a good understanding of:<\/p>\n<\/div>\n

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