Any deep learning network consists of four important components: Dataset, defining the model (network structure), Training/Learning, and Prediction/Evaluation. We can do all these in TensorFlow; let's see how:
- Dataset: DNNs depend on large amounts of data. The data can be collected or generated or one can also use standard datasets available. TensorFlow supports three main methods to read the data. There are different datasets available; some of the datasets we will be using to train the models built in this book are as follows:
- MNIST: It is the largest database of handwritten digits (0 - 9). It consists of a training set of 60,000 examples and a test set of 10,000 examples. The dataset is maintained at Yann LeCun's home ...
