Deep learning is aptly named as it is the subset of machine learning that learns as it operates, and machine learning is in turn a subset of
. Machine learning is a simpler tool, wherein the programmer gives the AI a set of parameters that it follows. Increased utilization of deep learning has the potential to greatly reduce the manual work of programming parameters for AI. Across several industries, greater use of deep learning algorithms is likely to enable more efficient expenditure of programmers’ time and energy.
Deep learning is most often found in virtual assistance, voice-enabled remotes, and emerging technologies such as self-driving cars. Its application requires substantial processing power, using GPUs with a high-performance capacity to handle the enormous number of calculations needed.
How is it different from Machine Learning?
Machine learning operates on more simplified data than deep learning, and uses structured data for its algorithms. Deep learning, however, eliminates much of the pre-processing involved with machine learning by being capable of ingesting unstructured data, such as texts, and extracting the necessary data. This also means that there is a difference in the skills required to code deep learning and machine learning.
Coding capabilities for deep learning algorithms are some of the most sought-after by large organizations around the world.
What is the current state of affairs?
There are a number of real-world, deep learning applications; however, they are often hidden behind the scenes, and the average user is unaware of the complexity of the automation at work. Deep learning is used in various fields, such as financial services, customer service, and healthcare. For example, financial services rely on a large amount of analytics to predict stock trading, monitor for fraud, and aid clients in building their investment portfolios.
The most prevalent sign of deep learning in customer services is in the number of
AI-enabled assistants or chatbots. These are more specialized than the average chatbot that cycles through manual responses. Deep learning chatbots seek to understand if there are multiple responses to ambiguous questions. Depending on the analysis of the chatbot, it will then provide a response or route to a human agent.
Healthcare is another field where deep learning has seen significant growth. Digitizing hospital records and images allows for much swifter organization and categorization of affairs, speeding up and increasing the efficiency of hospital work.
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