The Deep Learning project lifecycle begins with problem identification and data collection. The data is then preprocessed before a model is selected, trained, and evaluated for performance. Successful models are deployed into a production environment where they are continuously monitored and maintained. This entire process is iterative, with insights from later stages often leading to refinements in earlier steps. 

Full cycle of Deep Learning Project