Summary
In this chapter, we covered the TensorFlow ecosystem at a high level. We looked at some of the key components that make TensorFlow the platform of choice for building deep learning applications and solutions for many ML engineers, researchers, and enthusiasts. Next, we discussed what tensors are and how they are useful in our models. After this, we looked at a few ways of creating tensors. We explored various tensor properties and we saw how to implement some basic tensor operations with TensorFlow. We built a simple model and used it to make predictions. Finally, we looked at how to debug and solve error messages in TensorFlow and ML at large.
In the next chapter, we will look at regression modeling in a hands-on manner. We will learn how to extend our simple model to solve a regression problem for a company’s HR department. Also, what you have learned about debugging could prove useful in the next chapter – see you there.