Thinking-data-science-a-data-science-practitioners-guide

18 1 Data Science Process
Fig. 1.15 Transfer learning
process
We show the workflow for transfer learning model development in Fig. 1.15.
In our example, we use the pre-trained model for object recognition. Such models
are trained on millions of images and have taken up many weeks of training on
GPU/TPUs. Having such processing power and time for training at your disposal is
beyond the scope of many of us. Only the tech giants have infrastructures to do such
model training. Fortunately, they allow us to use their pre-trained models. We can
extend their models, add a few more layers, and train them for our further purpose,
like predicting the dog’s breed after the model detects a dog in an image. I will
provide you with a concrete example of how this is done later in the book.
Summary
I have introduced you to the complete data science process, followed by an advanced
data scientist. For building efficient ML models, you need to develop several skills.
The depth of each skill is usually shallow. To be a successful data scientist, what it
really requires is the knowledge width and not the depth. Finally, a data scientist is
not a researcher to develop new algorithms or visualization techniques. You need to
focus on understanding the concepts behind these technologies and how to use them
in building your own models. What is important is the decision which one to use
where, and that’s what this book is going to guide you into. Keep reading!