What is deep learning? Meaning of deep learning





Deep learning

Deep learning computer algorithms go through the same procedure as a child learning to recognize a dog

Deep Learning is a terminology often used interchangeably with machine learning; however, it is not the same. Machine learning is a type of artificial intelligence in which a computer learns to achieve something without being instructed to do so. DL, on the other hand, is the process by which machines learn to perform something using an artificial neural network made up of a number of layers arranged in a hierarchy.

What is deep learning?

Deep learning is a type of machine learning and artificial intelligence (AI) that mimics how people acquire specific types of knowledge. DL is a critical part of data science, which also relates to statistics and predictive models. Deep learning is especially beneficial for data scientists tasked with collecting, analyzing, and interpreting massive amounts of data; deep learning speeds up and simplifies this process.

Deep learning, in its most basic form, can be seen as a method of automated predictive analytics. Deep learning algorithms are stacked in a hierarchy of escalating complexity and abstraction, unlike typical machine learning algorithms, which are linear.

How does deep learning work?

Deep learning computer algorithms go through the same procedure as a child learning to recognize a dog. Each program in the hierarchy outputs a nonlinear map to its input and then uses what it learns to generate a statistical model as output. Iterations are repeated until the result has an acceptable degree of accuracy. The word deep was inspired by the number of processing layers that data has to go through.

The learning process in typical machine learning is controlled and the programmer has to be extremely detailed when instructing the computer on what kinds of things to look for in order to determine whether or not an image contains a dog. This is a time consuming procedure known as feature extraction, and the computer’s success rate depends entirely on the programmer’s ability to specify exactly a feature set for the dog. The advantage of deep learning is that the software creates the feature set unattended. Unsupervised learning is not only faster, but in most cases it is also more accurate.

Initially, the computer program can get training data, which is a collection of photos, each image of which a human has categorized as a dog or non-dog using meta tags. The software creates a feature set for dogs and builds a prediction model using the information received from the training data. In this scenario, the first model of the computer may suggest that everything in a picture with four legs and a tail should be called dog. Of course, the software does not know the terms ‘four legs’ or ‘tail’. It only looks for pixel patterns in the digital data. With each cycle, the forecasting model becomes more complex and accurate.

Unlike a baby, who can take weeks or even months to get the idea of ​​a dog, a computer program that uses deep learning techniques can be presented as a training set and search through millions of photos, recognizing exactly which images dogs are on. within a few minutes.

Deep learning systems require massive amounts of training data and processing power to achieve an acceptable level of accuracy, neither of which was widely accessible to programmers before the era of big data and cloud computing. DL programming is able to produce accurate prediction models from large amounts of unlabeled, unstructured data, as it can generate complicated statistical models directly from its own repetitive output. This is important as the Internet of Things (IoT) is becoming more common, as most of the data generated by humans and machines is unstructured and unlabeled.

Meaning of deep learning

Deep learning is all the rage these days, thanks to its superiority in terms of accuracy. DL is being aggressively invested by major tech companies as it has become vital in every industry as a means of making machines smarter. Google AlphaGo is just one example of deep learning that made headlines when it defeated Lee Sedol, one of the world’s top Go players.

Deep learning is widely used in Google’s search engine, speech recognition systems and self-driving cars. Google introduced Smart Reply, a deep learning network that composes short emails for you.

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