Deep learning is actually a subset of machine learning and it helps the modern systems such as computers to learn naturally by example just like humans. It is the key technology behind many things such as the innovation brought around the automotive industry in which cars can learn to operate without manual input and take on the read. These can not only learn to decelerate at a stop sign but can make turns, turn on blinkers and perform various other functions that a normal driver would be able to perform without any human input.
Voice control that allows people with accessibility issues use their smartphones or laptop device is backed up by deep learning, hands-free speakers and many other things are a clear subset of the deep learning which is a prosthetic of machine learning. Deep learning is getting a lot of attention nowadays and for good reason too, it has been achieving milestones which weren't even possible before.
In deep learning, a computer device is able to do classification related tasks directly from images, text, or sound. These models have the most technical accuracy which sometimes might exceed the human-level performance. These models are even trained by using a large set of clustered data and the neural architectures that in return have a lot of layers.
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Why Does Deep Learning Matter?
Deep learning matters because the level of accuracy it brings on the table is unmatched, it helps the businesses, digital enterprises, and consumer goods to reach the expectations of the users and is equally important for security-critical applications like the driverless cars. There have been various latest developments in the context of deep learning where it usually outsmarts the human actions of classifying the objects within images and separating text from an audio source.
There are two main reasons as to why deep learning has become successful only now and not before;
- Deep learning is a subset of machine learning that requires uninterrupted access to large amounts of the labeled data. Such as in the context of driverless cars it would require millions of images and thousands of hours of video to begin with.
- Deep learning also requires a substantial amount of computing power and any ordinary equipment is not going to help to get any advancements out of it. High performing GPUs have a parallel architecture that is extremely efficient for deep learning. Learning time can be effectively reduced when the deep learning technology is connected via cloud interface for the sake of retrieving as much data as the technology can and then by simulating the learned experience in the form of examples it is able to learn the behavior or solution to a problem.
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A Few Examples of Deep Learning Research
Deep learning applications are used in various industries from the automated driving to the medical devices and the uses have only begun to grow in number, there are still some areas that need to be explored right now.
- Automated driving is the most rigorous example of deep learning as here researchers are using the concepts of deep learning for the sake of detecting stop signs and traffic lights to detect the next thing the vehicles have to adapt to.
- Satellite technology has been laced with the latest prospects of deep learning that can inherently highlight the areas of interest, identify the safe zones
- or detecting unsafe zones for the troops so that a conflict could be avoided.
- Medical research is also not lurking behind at all as using deep learning in a variety of different studies and research programs to automatically detect cancer-causing cells. High dimensional data sets can be used to train a deep learning application to accurately detect the cancer cells so that the treatment can be made a little more specific over eradicating the cancerous cells rather than entangling the normal growth cells.
- In order to improve the worker’s safety around the heavy machinery deep learning is being employed in industrial automation. It can be done by making the applications run when people or objects are within an unsafe distance of the machines.
- Automated hearing and speech recognition are the subsets of deep learning that is widely used in electronics-based devices. Home assistance devices such as Alexa or various others use the concept of automated hearing and deep learning involved in learning the behavioral science of dealing with speech and other possible constructs.
Read more: What Is Machine Learning and how to start my career in it
How Does It Work?
Neural network architecture is the basis for most of the deep learning methods used out there. That is the reason as to why many deep learning methods are referred to as the deep neural networks. Deep refers to the number of hidden layers within a neural network where the traditional networks have only 2-3 hidden layers but deep networks can have as many as 150.
The neural network architecture starts computing the large labeled sets of data, processing them, and interpreting their sequence for the sake of deriving the insight that the data has to offer. Manual feature or insight extraction is not applicable here as deep learning is able to learn from the data that it is rendering without any external help or ties whatsoever.
Machine Learning and Deep Learning: The Difference
Deep learning is a specialized form of machine learning, the machine learning workflow would start with the relevant features being manually extracted from the images. The features are then used to create a specific model that can categorize the objects present within an image under observation. But with the deep learning workflow, the relevant features can be automatically extracted from the said images. Deep learning on the other hand is capable of performing end to end learning where a network is given raw data and a task to perform such as categorization of things and it learns to do this automatically.
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