Summary
TensorFlow is designed to make predictive analytics through ML and DL easy for everyone, but using it does require a decent understanding of some general principles and algorithms. The latest release of TensorFlow comes with lots of exciting new features, so we have tried to cover them so that you can use them with ease. In summary, here is a brief recap of the key concepts of TensorFlow that have been explained in this chapter:
- Graph: Each TensorFlow computation can be represented as a data flow graph, where each graph is built as a set of operation objects. There are three core graph data structures:
tf.Graph(https://www.tensorflow.org/api_docs/python/tf/Graph),tf.Operation(https://www.tensorflow.org/api_docs/python/tf/Operation
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