- By Ravichander Rajendran
It is no wonder, now we do hyperparameter optimization and feature selection in a more automated and robust way, to build better performing deep learning models at ease – the next big question is “What’s next?”. Are we aware of the limitations of the “State-of-the-art” model that we use? Does it really fit your needs, or are we just adjusting it to match our needs? How much do we know about its architecture? The immediate answer to all these questions is Neural Architecture Search (NAS).
NAS is an algorithmic-based approach to find the optimal design of the neural network that outperforms the hand-designed models, it goes with the principle “Better the design, Better the performance” and NAS helps to minimize the time and cost involved in design experimentation.
The h
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Ravichander Rajendran
Ravichander is a passionate data professional with close to a decade of experience in DWBI, Data Management and DataScience. He is currently leading a team of data analysis and data scientists in AstraZeneca and in the past, he worked with Capgemini, HCL and Cognizant for projects involving leading players in Banking & Financial services, Leisure & Travel. He loves to learn new tools & technologies and in his leisure time, he likes experimenting different technical areas with proof of concepts.
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