What is Deep Learning? Everything you need to know!!
What is Deep Learning?
Deep learning is a man-made intelligence that emulates the activities of the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of Artificial Intelligence that is fit for taking in solo from information that is unstructured or unlabeled. It is also known as deep neural learning or deep neural network. Deep learning requires substantial computing power. High-performance GPUs have a parallel architecture that are used efficiently for deep learning. When clusters or cloud computing combined with, this enables development teams to reduce training time for a deep learning network from weeks to hours or less.
Deep Learning is also a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep learning learns from vast amounts of unstructured data that would normally take humans decades to understand and process.
In, Deep learning we don’t have to program unequivocally everything. The idea of deep learning isn’t new. It has been around for a few years now. Over the most recent 20 years, Deep learning and AI came into the image.
How Deep Learning Works?
Deep learning has developed a vast connection with the advanced time, which has achieved a blast of information in all structures and from each locale of the world. This information, referred to just as large information, is drawn from sources like web-based life, web search tools, internet business stages, and online films, among others. This huge measure of information is promptly open and can be shared through applications like distributed computing.
Nonetheless, the information, which regularly is unstructured, is immense to such an extent that it could take a long time for people to grasp it and concentrate significant data. Organizations understand the amazing potential that can come about because of unwinding this abundance of data and are progressively adjusting to AI frameworks for robotized support.
Implementation of Deep learning
Deep learning is used used in industries from automation to research. Automated Driving, Aerospace and Defense, Medical Research, Cancer treatment, Industrial Automation, Object Detection, Electronics, Home assistance etc. are some example fields of Deep Learning Implementation.
Machine learning Vs Deep learning
Deep learning is a subset of machine learning which utilizes artificial neural networks to carry the process of machine learning. These artificial neural networks are built like the human brain, with neuron nodes connected like a web. While traditional programs build analysis with data in a linear way, the deep learning systems enables machines to process data with a nonlinear approach. Big data processing is made possible easily by machine learning. A key advantage of deep learning is that they continue to improve as the size of your data increases.
When choosing between machine learning and deep learning, you should have to consider whether you have a high-performance GPU with lots of labeled data. If you don’t have either of those things, it doesn’t make more sense to use machine learning instead of deep learning. Deep learning is generally more complex, so you’ll need at least a few thousand images to get the desired results. Having a high-performance GPU means the model will take less time to analyze all those images as it analyses the data model more effectively.
*Image Source : Internet