Summary

In this chapter, we briefly covered the TensorFlow library. We covered the TensorFlow data model elements, such as constants, variables, and placeholders, and how they can be used to build TensorFlow computation graphs. We learned how to create tensors from Python objects. Tensor objects can also be generated as specific values, sequences, or random valued distributions from various TensorFlow library functions. 

We covered the TensorFlow programming model, which includes defining and executing computation graphs. These computation graphs have nodes and edges. The nodes represent operations and edges represent tensors that transfer data from one node to another. We covered how to create and execute graphs, the order of execution, and how to execute graphs on multiple compute devices, such as CPU and GPU.

We also learned about machine learning and implemented a classification algorithm to identify the handwritten digits dataset. The algorithm we implemented is known as multinomial logistic regression. We used both TensorFlow core and Keras to implement the logistic regression algorithm.

Starting from the next chapter, we will look at many projects that will be implemented using TensorFlow and Keras.