Summary

In this chapter, we learned about the general training process for a neural network using a gradient-based optimization algorithm. We learned about CNNs and the classic LeNet CNN to solve the MNIST problem. We built this network, and learned how to add training and test layers to it, so that we could use it for training. We finally used this network to train and learned how to monitor the network during training using Caffe2. In the following chapters, we will learn how to work with models trained using other frameworks, such as Caffe, TensorFlow, and PyTorch.