We have covered basic, yet useful models for training classification tasks. We saw a simple model for an MNIST dataset with both Keras and TensorFlow APIs. We also saw how to utilize TensorBoard for watching the training process. Then, we discussed state-of-the-art architectures with some specific applications. Several ways to increase the accuracy such as data augmentation, training on bottleneck layers, and fine-tuning a pre-trained model were also covered. Tips and tricks to train models for new models were also presented.
In the next chapter, we will see how to visualize the deep learning models. We will also deploy the trained models in this chapter for inference. We will also see how to use the trained layers for the application of an image search through an application. Then, we will understand the concept of autoencoders and use it for the dimensionality of...
