The TensorFlow Way

In Chapter 1, Getting Started with TensorFlow 2.x we introduced how TensorFlow creates tensors and uses variables. In this chapter, we'll introduce how to put together all these objects using eager execution, thus dynamically setting up a computational graph. From this, we can set up a simple classifier and see how well it performs.

Also, remember that the current and updated code from this book is available online on GitHub at https://github.com/PacktPublishing/Machine-Learning-Using-TensorFlow-Cookbook.

Over the course of this chapter, we'll introduce the key components of how TensorFlow operates. Then, we'll tie it together to create a simple classifier and evaluate the outcomes. By the end of the chapter, you should have learned about the following:

• Operations using eager execution
• Layering nested operations
• Working with multiple layers
• Implementing loss functions
• Implementing backpropagation
• Working with batch and stochastic training
• Combining everything together

Let's start working our way through more and more complex recipes, demonstrating the TensorFlow way of handling and solving data problems.