Different types of neural network

So far, we have learned what feed-forward neural networks look like and how techniques such as backpropagation and gradient descent are applied to it in order to optimize their training process. The binary classification problem we studied earlier appears to be too naive and too impractical, doesn't it?

Well, there are many problems that a simple NN model can solve well. But as the complexity of the problem increases, improvements to the basic NN model become necessary. These complex problems include object detection, object classification, image-caption generation, sentiment analysis, fake-news classification, sequence generation, speech translation, and so on. For problems like these, a basic NN model is not sufficient. It needs some architectural improvements so that it can solve these problems. In this section, we are going to study two of the most powerful and widely used NN models—convolutional neural networks and recurrent neural networks. At the heart of the stunning applications of deep learning that we see nowadays sit these NN models.