In principle, Deep Learning (DL) is a special part of machine learning, which takes advantage of advanced neural networks in order to solve challenging problems, such as the identification and classification of very complex patterns. Its uniqueness lies in the use of neural networks with many hidden layers as a means of simulating the human brain and providing human-like reasoning. A growing number of innovative applications, including the famous self-driving car, will integrate DL models in the coming years, which illustrates the importance of understanding DL and when and how to use it.
DL is not a new technology as it has been around for nearly twenty years and is already used for handwriting interpretation and network security. However, during the last couple of years the interest around DL has exploded for a number of reasons, including:
These trends are driving research and development investments in DL, which is gradually leading to more innovative deep learning approaches. At the same time, tech giants such as Google have recently acquired deep learning enterprises in order to back up their AI products and services.
The growing sophistication of deep learning techniques gives rise to its integration in pragmatic applications, notably applications that identify complex patterns and enable human like reasoning. A prominent set of applications that involve DL-based pattern detection includes:
These applications are indicative of the nature of DL systems. Potential deployments of DL are virtually unlimited and include adding sounds to silent movies, automated game playing, automatic generation of text, processing of text in the wild, automatic generation of image captions and many more. Furthermore, these applications are integrated in wider and more complex systems, such as autonomous vehicles, smart buildings and industrial automation systems. All major vendors are also using some sort of DL technology in their applications e.g., Google integrates AI in its search engine, while Facebook uses DL in some of its social products and services.
The surge of interest around AI has also led to the emergence of various DL frameworks and tools:
Deep learning signals a revolution in the design and development of AI and BigData applications, which will enable capabilities that are not possible nowadays. Fortunately, there is already a pool of deep learning tools, which can facilitate development and deployment.
If you are planning for the development of a novel automation system or the deployment of robots to support your business processes. Or, If you are in need of identifying sophisticated patterns within very large datasets then, you can start acquainting yourself with deep learning and deep learning tools. These have now become a significant part of Machine Learning, AI and BigData processing and will continue to evolve to play a more critical role.
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