In this chapter, we went through building a learning system for fish recognition; we also saw how we can build complex applications, such as fish recognition, using a few lines of code with the help of TensorFlow and Keras. This coding example was not meant to be understood from your side, rather to demonstrate the visibility of building complex systems and how data science in general and specifically deep learning became an easy-to-use tool.
We saw the challenges that you might encounter in your daily life as a data scientist while building a learning system.
We also looked at the typical design cycle for building a learning system and explained the overall idea of each component involved in this cycle.
Finally, we went through different learning types, having big data generated daily by big and small companies, and how this vast amount of data raises a red alert to build scalable tools to be able to analyze and extract value from this data.
At this point, the reader may be overwhelmed by all the information mentioned so far, but most of what we explained in this chapter will be addressed in other chapters, including data science challenges and the fish recognition example. The whole purpose of this chapter was to get an overall idea about data science and its development cycle, without any deep understanding of the challenges and the coding example. The coding example was mentioned in this chapter to break the fear of most newcomers in the field of data science and show them how complex systems such as fish recognition can be done in a few lines of code.
Next up, we will start our by example journey, by addressing the basic concepts of data science through an example. The next part will mainly focus on preparing you for the later advanced chapters, by going through the famous Titanic example. Lots of concepts will be addressed, including different learning methods for regression and classification, different types of performance errors and which one to care about most, and more about tackling some of the data science challenges and handling different forms of the data samples.
