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Deep Learning Techniques and Optimization Strategies in Big Data Analytics
The method of modelling the architecture search space as a neural network, with each choice being represented as a path in the network.
Published in Chapter:
Deep Learning Techniques and Optimization Strategies in Big Data Analytics: Automated Transfer Learning of Convolutional Neural Networks Using Enas Algorithm
Murugan Krishnamoorthy (Anna University, India), Bazeer Ahamed B. (Balaji Institue of Technology and Science, India), Sailakshmi Suresh (Anna University, India), and Solaiappan Alagappan (Anna University, India)
DOI: 10.4018/978-1-7998-1192-3.ch009
Abstract
Construction of a neural network is the cardinal step to any machine learning algorithm. It requires profound knowledge for the developer in assigning the weights and biases to construct it. And the construction should be done for multiple epochs to obtain an optimal neural network. This makes it cumbersome for an inexperienced machine learning aspirant to develop it with ease. So, an automated neural network construction would be of great use and provide the developer with incredible speed to program and run the machine learning algorithm. This is a crucial assist from the developer's perspective. The developer can now focus only on the logical portion of the algorithm and hence increase productivity. The use of Enas algorithm aids in performing the automated transfer learning to construct the complete neural network from the given sample data. This algorithm proliferates on the incoming data. Hence, it is very important to inculcate it with the existing machine learning algorithms.
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