Summary
In this chapter, you learned what it takes to complete a deep learning project successfully in a repeatable and consistent manner at a somewhat mid to high-level view. The topics we discussed in this chapter were structured to be more comprehensive at the earlier stages of the deep learning life, which covers the planning and data preparation stages.
In the following chapters, we will explore the mid to final stages of the life cycle more comprehensively. This involves everything after the data preparation stage, which includes model development, model insights, model deployment, and, finally, model governance.
In the next chapter, we will start to explore common and widely used deep learning architectures more extensively.
