Artificial Intelligence and Machine Learning have been buzz words for more than a decade now, which makes the machine an artificially intelligent one. The computational speed and enormous amounts of data have stimulated academics to deep dive and unleash the tremendous research potential that lies within. Even though Machine Learning helped us start learning intricate and robust systems. Deep Learning has curiously entered as a subset for AI, producing incredible results and outputs in the field.
Deep Learning architecture is built very similar to the working of a human brain, whereby scientists teach the machine to learn in a way that humans learn. This definitely is a tedious and challenging task, as the working of the human brain itself is a complex phenomenon. Our research in the field has resulted in valuable outcomes to makes things easily understandable for scholar and scientists to build worthy applications for the welfare of society. They have made the various layers in neural nets in Deep Learning auto-adapt and learn according to the volume of datasets and complexity of algorithms.
The efficacy of Deep Learning algorithms is in no way comparable to traditional Machine Learning helped industrialists to deal with unsolved problems in a convincing way, opening a wide horizon with ample opportunity. Natual language processing, speech and image recognition, the entertainment sector, online retailing sectors, banking and finance sectors, the automotive industry, chat bots, recommender systems, and voice assistants to self-driving car are some of the major advancements in the field of Deep Learning.
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