Recently, we have seen a huge adoption of neural networks in a variety of applications. In this section, let's try to understand the reason why adoption might have increased considerably. Neural networks can be architected in multiple ways. Here are some of the possible ways:
The box at the bottom is the input, followed by the hidden layer (the middle box), and the box at the top is the output layer. The one-to-one architecture is a typical neural network with a hidden layer between the input and output layer. Examples of different architectures are as follows:
| Architecture | Example |
| One-to-many | The input is an image and the output is a caption for the image |
| Many-to-one | The input is a movie review (multiple words) and the output is the sentiment associated with the review |
| Many-to-many | Machine translation of a sentence in one language to a sentence in another language |
Apart from the preceding points, neural networks are also in a position to understand the content in an image and detect the position where the content is located using an architecture named Convolutional Neural Network (CNN), which looks as follows:
Here, we saw examples of recommender systems, image analysis, text analysis, and audio analysis, and we can see that neural networks give us the flexibility to solve a problem using multiple architectures, resulting in increased adoption as the number of applications increases.