Deep learning is a subset of machine learning that processes data and creates patterns for use in decision making. Deep learning techniques teach machines to perform tasks that would otherwise require human intelligence to complete.
Neural Networks, Machine Learning, Data and Complex Algorithms
Deep learning encompasses several technologies including multi-layered artificial neural network architectures to deliver accurate object detection, speech recognition and language translation. The structure of a neural network resembles the networked structure of neurons in the brain, with layers of connected nodes that can learn from data and be trained to recognize patterns, classify data, and predict events.
Deep learning models use machine learning, a type of artificial intelligence (AI) where machines can learn by experience without human involvement. Deep learning also uses complex algorithms, inspired by the human brain and how it works, to learn from large amounts of labeled data.
Large data sets and neural network architecture is how deep learning models learn directly from the data without the need for manual extraction. Deep Learning technology is evolving quickly, due largely in part to the staggering amount of data we generate every day. Deep learning networks continue to improve as the size of your data increases.
Growing data resources and advances in computing power that benefit deep learning algorithms has helped to evolve this technology quickly.
Examples of Deep Learning
Today, deep learning research is a driving force behind many of the technologies we use every day -- from the voice control functions found in our smart devices to self-driving cars. Often, the end-user benefits are so integrated into devices that people may not even realize that deep learning algorithms and AI are deeply embedded in many of the online services and apps we use every day.
Both Netflix and Amazon use deep learning algorithms to suggest products and shows to watch, intelligent virtual assistants (Alexa, Bixby, Cortana, Google Assistant or Siri) use deep learning to understand speech and the language humans use when they interact with them. Other examples of deep learning include colorization of black and white Images, autonomous vehicles, translators, facial recognition, classification and medical disease diagnoses.
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