The Deep Learning Development Lifecycle
Deep Learning development can be illustrated as a cycle of various steps, starting from data collection to ultimately deploying an app or service to users. Once a Deep Learning model is deployed, monitoring its performance and how users interact with its functionality will inform what new data should be collected or what additional processing should be implemented to rebuild then redeploy a more performant model.

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