neural networks and deep learning deep learning has become one of the most well-known representations of machine learning in the recent years deep learning applications have achieved remarkable accuracy and popularity in various fields especially in image and audio related domains python is the language of choice when it comes to learning deep networks and complex representations of data in this sectionwe briefly discuss anns (artificial neural networksand deep learning networks then we will move on to the popular deep learning frameworks for python sincethe mathematics involved behind anns is quite advanced we will keep our introduction minimal and focused on the practical aspects of learning neural network we recommend you check out some standard literature on the theoretical aspects of deep learning and neural networks like deep learning by goodfellow and bengioif you are more interested in its internal implementations the following section gives brief refresher on neural networks and deep learning based on what we covered in detail in artificial neural networks deep learning can be considered as an extension of artificial neural networks (annsneural networks were first introduced as method of learning by frank rosenblatt in although the learning model called perceptron was different from modern day neural networkswe can still regard the perceptron as the first artificial neural network artificial neural networks loosely work on the principle of learning distributed distribution of data the underlying assumption is that the generated data is result of nonlinear combination of set of latent factors and if we are able to learn this distributed representation then we can make accurate predictions about new set of unknown data the simplest neural network will have an input layera hidden layer ( result of applying nonlinear transformation to the input data)and an output layer the parameters of the ann model are the weights of each connection that exist in the network and sometimes bias parameter this simple neural network is represented as shown in figure -