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By lavinaagarwal |Email | Apr 26, 2018 | 17112 Views

1. It runs on an artificial neural network.
ANN, as the name suggests, these are computer systems that are roughly patterned after the human brain. The brain has lots of interconnected neurons that transmit signals to one another, and artificial neural networks have lots of interconnected nodes, also called neurons, that transmit data to one another. In the case of deep learning, those nodes are arranged in layers, with neurons passing information from one layer to another.

 2. It needs huge amounts of resources.
Systems with highly advanced graphics processing units, which are very good processing deep learning workloads, are much more affordable. And cloud computing vendors are making those systems available on a pay as you go basis, which puts them within reach for almost any organization.
Deep learning also requires huge volumes of data for training, and enterprises ever increasing stores of big data are perfect for this purpose.

 3. Deep learning is new in its field of technology.
In the years since, the technique has been applied to a lot of different problems, and it has proven remarkably adept at training computers to do a variety of things that have always been easy for humans but fairly difficult to teach to machines. Many AI researchers believe that deep learning will continue to be very influential in the coming years, but others, including Marcus, think that deep learning is of limited usefulness and will need to be supplemented by a lot of other techniques as AI research progresses.

4. Deep learning is a part of machine learning.
Deep learning is a specialized type of machine learning that uses a hierarchical approach. It s a set of algorithms that has proven particularly good at solving certain types of computing problems that are difficult to define with explicit programming.

5. Deep learning makes popular forms of AI potential.
Deep learning overcomes those limitations by having the computer figure out for itself which features in the training data are important for the task at hand, whether that task is identifying images, interpreting audio, or figuring out which movie you might like to watch next.

6. Deep learning can be supervised or unsupervised.
In unsupervised deep learning, you would show the computer a whole lot of images without labels. The machine would not be able to figure out the label on its own, but it would be able to group together all the similar images. This unsupervised learning can be very helpful in cases where all your test data is not labeled or when you don t know what you are looking as in many data mining applications.

7. Deep learning has a lot of layers as the name suggests.
The name deep learning refers to the way in which these algorithms work, which is to process data through many layers. They take an input, create an output, and then use that output as the input for the next layer of learning. It starts by making very small abstractions or generalizations and then moving up to broader generalizations.


Source: HOB