before we beginthe mathematical building blocks of neural networks two quantities are displayed during trainingthe loss of the network over the training dataand the accuracy of the network over the training data we quickly reach an accuracy of ( %on the training data now let' check that the model performs well on the test settootest_losstest_acc network evaluate(test_imagestest_labelsprint('test_acc:'test_acctest_acc the test-set accuracy turns out to be %--that' quite bit lower than the training set accuracy this gap between training accuracy and test accuracy is an example of overfittingthe fact that machine-learning models tend to perform worse on new data than on their training data overfitting is central topic in this concludes our first example--you just saw how you can build and train neural network to classify handwritten digits in less than lines of python code in the next 'll go into detail about every moving piece we just previewed and clarify what' going on behind the scenes you'll learn about tensorsthe data-storing objects going into the networktensor operationswhich layers are made ofand gradient descentwhich allows your network to learn from its training examples licensed to