What actually is deep learning? It is a buzzword in neural network technology. What is a neural network then? An artificial neural network is a computer software model that replicates the behavior of neurons in the human brain. A neural network is one way to classify data. For example, if we want to classify an image based on whether it contains an object or not, we can use this method.

There are several other computer software models for classification such as logistic regression and Support Vector Machine (SVM); a neural network is one of them. So, why are we not calling it a neural network instead of deep learning? The reason is that, in deep learning, we use a large number of artificial neural networks. So, you may ask, why was it not possible before? The answer is: to create a large number of neural networks (multilayer perceptron), we may need a high amount of computational power. So, how has it become possible now? It's because of the availability of cheap computational hardware. Will computational power alone do the job? No, we also need a large dataset to train with.

When we train a large set of neurons, it can learn various features from the input data. After learning the features, it can predict the occurrence of an object or anything we have taught to it.

To teach a neural network, we can either use the supervised learning method or go unsupervised. In supervised learning, we have a training dataset with input and its expected output. These values will be fed to the neural network, and the weights of the neurons will be adjusted in such a way that it can predict which output it should generate whenever it gets particular input data. So, what about unsupervised learning? This type of algorithm learns from an input dataset without having corresponding outputs. The human brain can work in a supervised or unsupervised way, but unsupervised learning is more predominant in our case. The main applications of deep neural networks are in the classification and recognition of objects, such as image recognition and speech recognition.

In this book, we are mainly dealing with supervised learning for building deep learning applications for robots. The next section will give us an introduction to deep learning for robotics.