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Deep Learning—a subset of artificial intelligence (AI) —is an algorithm that is capable to learn anything from its surroundings based on the data fed to it. There is no theoretical limit to what it can learn as it completely functions on data. All forms of structured and unstructured data can be comprehended by the technology making it of great use and industries have also shown immense interest after the success of big data. It is essentially a neural network with many layers; these models are enormous. The algorithm is not new, but the growth of hardware and computational power has resulted in an upswing of its development. The R&D is paving the path for new applications such as computer vision and speech to text with complete accuracy.
Organizations interested to incorporate the technology must understand that deep learning is not much complex as standalone applications but its three verticals might be.
• Big data: expensive to collect, label, and store.
• Big models are hard to optimize
• Big compute is expensive
Check Out:- SemiConductor Review
Big Data: Enterprises already have big data model incorporated in their businesses but for deep learning to operate nothing can be termed as sufficient. As there are no standard node formations that define the outcomes put forward by the algorithm, there is no limit of data that will be required for 100 percent accuracy. Natural language processing, IoT applications, and computer vision, all these applications generate and require voluminous datasets which further will require equally bid infrastructure for storage and processing. There exist solutions to data storage and processing; the only drawback is that they are expensive.
Big Models: Big models are enormous -often more than 50 million parameters—which makes it a tough nut crack for optimization. There various ways for the same. In case of insufficient labeled data, transfer learning can be utilized where knowledge from a related and existing task is transferred to the new task. A better-fitting model is likely to be a transfer learning model. Researchers believe that the rate of growth is steeper but is certain to improve with time. To optimize big models organizations will have to continue to explore new algorithms.
Big Compute: As the data is limitless in deep learning, the requirement of computational power grows rapidly. Every new dataset fetches some power from existing which in case of large datasets might slow down the process of learning. So organizations will have to continually invest to maintain high computational power.