Getting Deep Learning working in the wild


Data-centric learning resources are somewhat scattered today, and that’s why we developed a new Data Centric Deep Learning course on the co:rise education platform. It is an introduction to a set of approaches and best practices, for people who are trying to do deep learning in the wild.

Have you been excited by recent high profile deep learning successes, but not sure how to practically keep deep learning models working for your project? We’ve developed a distilled set of materials on data-centric deep learning approaches – which are often among the most impactful tools to get deep learning models working on new tasks.

Data-centric deep learning is a relatively new area and a broad term. For us, being data-centric means taking a different perspective on deep learning that’s centered around building and maintaining the datasets which define and evaluate deep learning models. The real-world applications and successes of deep learning systems are growing by the day. Modern deep learning image and text categorization performance is at parity with human experts, and deep learning models process complex transactional or financial data to make inferences about future behavior.

Recent years have seen a proliferation of deep learning model architectures to meet the needs of different tasks. In parallel to these model-centric developments, experience building and deploying deep learning systems has repeatedly demonstrated the need for a

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