In this chapter, the concepts of transfer learning and fine-tuning were introduced. Training a very deep convolutional neural network from scratch, starting from random weights, requires the correct equipment, which is only found in academia and some big companies. Moreover, it can be a costly process since finding the architecture that achieves state-of-the-art results on a classification task requires multiple models to be designed and trained and for each of them to repeat the training process to search for the hyperparameter configuration that achieves the best results.
For this reason, transfer learning is the recommended practice to follow. It is especially useful when prototyping new solutions since it speeds up the training time and reduces the training costs.
TensorFlow Hub is the online library offered by the TensorFlow ecosystem. It contains an online catalog...
