Neural network models in neuroscience allow one to study how the connections between neurons shape the activity of neural circuits in the brain.
Deep neural network models are heavily resource-intensive, all the more when ensembles of multiple models are involved. Each retinal image may take seconds to analyze, including preprocessing steps, even with GPUs available.
Convolution neural network model or CNN is one of the most popular models used for natural language processing. The most important advantage that this model carries is that it can mechanically detect significant characteristics by itself. CNN also proves to be proficient in calculations as well.
A neural network model is represented by its architecture that shows how to transform two or more inputs into an output. The transformation is given in the form of a learning algorithm. In this work, the feed-forward architecture used is a multilayer perceptron (MLP) that utilizes back propagation as the learning technique.
A neural net is supposed to model a X/Y relation. The problem is the generalisation on unknown Y's. It is very important to understand this fact, because we often see neural network models that is perfect in respect to the actual X/Y being used in the learning.
A neural network model is represented by its architecture that shows how to transform two or more inputs into an output. The transformation is given in the form of a learning algorithm. In this work, the feed-forward architecture used is a multilayer perceptron (MLP) that utilizes back propagation as the learning technique. The difference from other mathematical models like E-model is that this model is able to retrain to learn a new relationship of voice quality and impairment factor (Sun, 2004 ). This is a great benefit for IP networks in which various parameter factors are not constant. In particular, a three-layer MLP neural network is created from the open-source Waikato Environment for Knowledge Analysis (Weka) software ( Hall et al., 2009 ). The input nodes are the impairment parameters consisting of packet-loss rate, delay, codec types, gender, and language, while the output node is the predicted voice quality, MOS-CQO. The number of hidden nodes is optimized by trial-and-error analysis to be five nodes, and the model is created from the training data set using tenfold cross-validation. The structure of the neural network model used is shown in Figure 5.6.
It is particularly convenient to use a neural network as the function approximator for reinforcement learning because neural networks can be trained in an online fashion (i.e. , one training example at a time), allowing them to be updated after each action. In recent years, deep neural networks have become increasingly popular for this, allowing for better scaling to large and complex problems. Many recent successful solutions to games have made use of reinforcement learning for training, including results on Atari games [ 17] and Go [ 18 ].
Convolution neural network model or CNN is one of the most popular models used for natural language processing. The most important advantage that this model carries is that it can mechanically detect significant characteristics by itself. CNN also proves to be proficient in calculations as well. They can be executed in any machine and bear the speciality of using special convolution and pooling operations. The term convolution represents the mathematical functionality of unification of two information sets. CNN maintains the nonlinearity feature as should be in an effective neural network. Pooling is used to reduce the dimensionality by dropping the amount of factors and hence shortening the time taken for execution. CNN is trained using backpropagation with gradient descent. There are two parts in the CNN model, namely, mining of features and categorizing them accordingly, and the convolution layers act as the major motivating force of the CNN model.
Neural computing should be used as it provides a powerful alternative to traditional statistical methods for the prediction of responses. If only generalisations are wanted the neural network computes this more easy in a well defined function. If diagnostic tools are essential together with generalisations, a mixture of linear and non linear methods should be taken into account.
Pooling is used to reduce the dimensionality by dropping the amount of factors and hence shortening the time taken for execution.
Deep neural network models are heavily resource-intensive, all the more when ensembles of multiple models are involved. Each retinal image may take seconds to analyze, including preprocessing steps, even with GPUs available. Given that many retinal images may need to be analyzed for each patient, possibly for multiple conditions requiring different models, the total time required adds up quickly. This has implications when turnaround timings are mandated. Commercial server hosting may moreover be prohibited due to security concerns, resulting in high upfront startup costs. Remote hosting of models may also be entirely infeasible in less-developed environments. In such circumstances, lightweight compressed models may be appropriate [72].