Deep neural networks (DNN) have produced groundbreaking results in many complex applications of AI, such as natural language processing, facial recognition, sentiment analytics and object recognition. For instance, the accuracy of Google's machine translation system improved 60% using a DNN approach. Finding the right network architecture – that is, the components of the network and how they are instantiated and connected – is essential to this process. If the architecture is chosen based on history and convenience, the network will not reach its full potential. Much of the recent research in DNNs has focused on designing specialized architectures that excel with specific tasks.
Apr-3-2019, 03:46:01 GMT