# Bioacoustic Classification of Antillean Manatee
### Machine Learning and Deep Learning  
In the field of machine learning, neural networks (NN) are one of the most popular methods for achieving supervised classification, that is, the task of learning to map the characteristics from a set of inputs and their corresponding output values/classes. The computation of characteristics is done in connected units, called neurons, which are arranged in different layers. The first layer is the _input layer_ where the input values or a representation of them are set; there can be one or more _hidden layers_ where intermediate nonlinear computations are carried out; and lastly an _output layer_ that condense all the computations resulting from the different neurons and the weights (values) for their connections. As research progressed, researchers realized that having more intermediate or _hidden layers_ helped to learn characteristics more efficiently, therefore, changes were made to the NN architectures, allowing for the presence of many more layers, with fewer neurons per layer, thus being spread deeply forward, these were called deep neural networks (DNN). In contrast, traditional neural networks (NN) or shallow networks had few layers with a great number of neurons [11]. One of the main features of the DNN is that the values computed on a neuron in the input layer will be re-used in later computations in the subsequent layers, with each layer helping in the refining of the value, thus enhancing the quality of the output and finally aiding the learning process in recognizing different patterns for classification. DNNs are very flexible, easily working on problems with massive amounts of data and are great at approximating very nonlinear functions, thus creating nonlinear supervised classifiers [12]. In the task of image classification, _deep_ convolutional neural networks, also called convolutional neural networks (CNN), have been established as one of the most important algorithms for understanding image content [13]. They mimic the work of the neurons in the receptive fields on the primary visual cortex of a biological brain [14]. In the case of digital images they work by passing visual information that reside in the image pixels by different hidden layers, with weighted connections and specialized filters, to extract relevant characteristics of the image [15]. There are four main operations and layer structures that make up the basis of the operation of a CNN: (1) _convolutional layers_: Its main purpose is the extraction of characteristics from an image and of a set of trainable filters. Their application allows for certain characteristics to be highlighted and become dominant in the output image, (2) _pooling_: It is used to reduce the dimensions of the images losing the least amount of information possible. It can keep the highest or the average pixel value of a portion of the image, (3) _rectified linear unit (ReLU)_: The rectifier is used just after each convolutional layer, as an operation that replaces negative values with zero and allows non-linearity to be added to the model and (4) _fully connected (dense) layer_. It performs the classification based on the characteristics extracted by the convolution and pooling process. In these layers, all its nodes are connected to the preceding layer. Three factors were key to the development and general adoption of these types of CNNs. The first factor being the use of specialized graphics processing units (GPUs) and distributed computing to speed up the training and overall calculations. As a consequence of the use of this new hardware architectures, the programmers were able to create more complex models, in which more hidden layers per layer were added [16]. Secondly, the win sought by the AlexNet model in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), an 8-layer neural network architecture with a mix of convolutional, max-pooling and fully connected layers and using ReLU as the activation function. This model outperformed the other competitors error rate by ten points [17].