At Google, we have successfully applied deep learning models to many applications, from
|Our GoogleNet architecture. Design of this network required many years of careful experimentation and refinement from initial versions of convolutional architectures.
To make this process of designing machine learning models much more accessible, we’ve been exploring ways to automate the design of machine learning models. Among many algorithms we’ve studied, evolutionary algorithms  and reinforcement learning algorithms  have shown great promise. But in this blog post,
Source:: Using Machine Learning to Explore Neural Network Architecture
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Michael founded Contrado Digital in 2013. He has experience working with national and multi-national brands in a wide range of industries, helping them achieve awesome results. Michael regularly speaks at local universities and industry events while keeping up with the latest trends in the digital industry.