Deep Belief Network RBM is a single-layered neural network. Instead of manually labelling data by humans, RBM automatically sorts through data; by properly adjusting the weights and biases, an RBM is able to extract important features and reconstruct the input. . GANs can be taught to create parallel worlds strikingly similar to our own in any domain: images, music, speech, prose. Together with convolutional Neural Networks, RNNs have been used as part of a model to generate descriptions for unlabelled images. It is assumed that the number of related tasks to be processed is N, and it is assumed that the size of the subset (that is, the ratio of the number of nodes in the subset to the number of nodes in the entire last hidden layer) is α, then 1/(N-1) > α > 1/N. CAP depth for a given feed forward neural network or the CAP depth is the number of hidden layers plus one as the output layer is included. • DBN was exploited to select the initial parameters of deep neural network (DNN DL models produce much better results than normal ML networks. Regional transport of atmospheric pollutants may be an important factor that affects the concentrations of air pollutants. Deep learning consists of deep networks of varying topologies. it is the training that enables DBNs to outperform their shallow counterparts. CNN have been the go to solution for machine vision projects. According to some research results, we let the factors that may be relevant to the concentration forecasting of three kinds of air pollutants make up a set of candidate features. The main purpose of a neural network is to receive a set of inputs, perform progressively complex calculations on them, and give output to solve real world problems like classification. A DBN is a sort of deep neural network that holds multiple layers of latent variables or hidden units. The network needs not only to learn the commonalities of multiple tasks but also to learn the differences of multiple tasks. The idea behind convolutional neural networks is the idea of a “moving filter” which passes through the image. For example, SO2 and NO2 are related, because they may come from the same pollution sources. Deep Belief Network(DBN) have top two layers with undirected connections and lower layers have directed connections Deep Boltzmann Machine(DBM) have entirely undirected connections. The reason is that they are hard to train; when we try to train them with a method called back propagation, we run into a problem called vanishing or exploding gradients.When that happens, training takes a longer time and accuracy takes a back-seat. In this paper, based on the powerful representational ability of DBN and the advantage of multitask learning to allow knowledge transfer, a deep neural network model with multitask learning capabilities (MTL-DBN-DNN), pretrained by a deep belief network (DBN), is proposed for forecasting of nonlinear systems and tested on the forecast of air quality time series. The weights from the trained DBN can be used as the initialized weights of a DNN [8, 30], and, then, all of the weights are fine-tuned by applying backpropagation or other discriminative algorithms to improve the performance of the whole network. As soon as you start training, the weights are changed in … For the sake of fair comparison, we selected original 1220 elements contained in the window before sliding window begins to slide forward, and used samples corresponding to these elements as the training samples of the static prediction models (DBN-DNN and Winning-Model). RNNSare neural networks in which data can flow in any direction. At this stage, the RBMs have detected inherent patterns in the data but without any names or label. If we want to predict the next word in a sentence we have to know which words came before it. A DBN works globally by fine-tuning the entire input in succession as the model slowly improves like a camera lens slowly focussing a picture. Here's a quick overview though- A neural network works by having some kind of features and putting them through a layer of "all or nothing activations". There is no clear threshold of depth that divides shallow learning from deep learning; but it is mostly agreed that for deep learning which has multiple non-linear layers, CAP must be greater than two. The MTL-DBN-DNN model can fulfill prediction tasks at the same time by using shared information. Practical Experiments. After the current concentration was monitored, the sliding window moved one-step forward, the prediction model was trained with 1220 training samples corresponding to the elements contained in the sliding window, and then the well-trained model was used to predict the responses of the target instances. Adding layers means more interconnections and weights between and within the layers. The most studied problem is the concentration prediction. A MI Tool box, a mutual information package of Adam Pocock, was used to evaluate the importance of the features according to the mRMR criterion. It is worth mentioning that learning tasks in parallel to get the forecast results is more efficient than training a model separately for each task. The sigmoid function is used as the activation function of the output layer. For example, human face; adeep net would use edges to detect parts like lips, nose, eyes, ears and so on and then re-combine these together to form a human face. 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