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Over the past few years, deep learning has become another trendy word1. It is mostly used in a business language when the conversation is about Machine Learning, Artificial Intelligence, Big Data, analytics, etc. Currently, it is showing great promise when it comes to developing the autonomous, self-teaching systems which are revolutionising many industries. Therefore I decided to write an article about deep learning startups, use cases and books.
Deep Learning was developed as a Machine Learning approach to deal with complex input-output mappings. Deep learning crunches more data than machine learning and that is the biggest difference. If you have a little bit of data, machine learning is a good choice, but if you have a lot of data, deep learning is a better choice for you. Deep learning algorithms do complicated things, like matrix multiplications. They also learn high-level features, so in the case of facial recognition, the algorithm will get the image pretty close to the RAW version in replication whereas machine learning’s images would be blurry. Another powerful feature is that it forms an end-to-end solution instead of breaking a problem and solution down into parts.
What is Deep Learning?
But what is Deep Learning exactly? Why has it become so popular? In …
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