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 has been running Contrado Digital for over 10 years and has over 15 years experience working across the full range of disciplines including IT, Tech, Software Development, Digital Marketing, Analytics, SaaS, Startups, Organisational and Systems Thinking, DevOps, Project Management, Multi-Cloud, Digital and Technology Innovation and always with a business and commercial focus.
He has a wealth of experience working with national and multi-national brands in a wide range of industries, across a wide range of specialisms, helping them achieve awesome results. Digital transformation, performance and collaboration are at the heart of everything Michael does.