So far, I have written two blog posts on how to use numpy to classify images of dogs and cats. In this blog post, I intend to show another way to classify images and I think the code is more simplistic, so it should be suitable for someone who does not want to be bogged down with lots of code.
A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain. It creates an adaptive system that computers use to learn from their mistakes and improve continuously. Thus, artificial neural networks attempt to solve complicated problems, like summarising documents or recognizing faces, with greater accuracy. Neural networks can help computers make intelligent decisions with limited human assistance. This is because they can learn and model the relationships between input and output data that are nonlinear and complex.
In this application of a neural network, I intend to explore the concept of computer vision. Computer vision is the ability of computers to extract information and insights from images and videos. With neural networks, computers can distinguish and recognize images similar to the way humans do.
Neural networks are composed of three layers, being:-
- Input layer. Information from the outside world enters the artificial neural network from the input layer. Input nodes process the data, analyse or categorise it, and pass it on to the next layer.
- Hidden layer. Hidden layers take their input from the input layer or other hidden layers. Artificial neural networks can have a large number of hidden layers. Each hidden layer analyses the output from the previous layer, processes it further, and passes it on to the next layer.
- Output layer. The output layer gives the final result of all the data processing by the artificial neural network. It can have single or multiple nodes. For instance, if we have a binary (yes/no) classification problem, the output layer will have one output node, which will give the result as 1 or 0. However, if we have a multi-class…