Deep Learning with Keras This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks ...
This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020.
This book introduces trending deep learning concepts and their use cases with an industrial and application-focused approach.
In the last half-decade, a new renaissance of machine learning originates from the applications of convolutional neural networks to visual recognition tasks.
... layer weights [27]. As a result of this, slow learning or overfitting can occur during training. Enlarging the network, increasing the number and quality of training samples, and techniques for avoiding the local minima, will not stretch ...
... recognition method that is a replication of biological neural network . However , this method only utilizes a limited set of characteristics from Inputs X1 X2 22 Xt - 1- Input layer Σif 56 3 3 - Domain Modelling Artificial Neural Network ( ...
Machine Learning and Neural Networks Chao Wang. layer - by - layer training method of DBN can optimize the weights of ... three black rectangles representing the three hidden layers , and within each black rectangle there are ...
Fundamentals and Techniques Frank Y. Shih. A more complicated neural network could be constructed by extending DPLMs and their connections . By assigning various combinations of Boolean functions , a neural network may act in quite ...
... learning algorithm without teacher demonstration, the structure parameters of the network are chosen as follows: the ... three-layer heterogeneous feedforward neural network model proposed by Robert Hecht-Nielson in 1987. The model ...
... learning algorithms to classify facial expressions in the images. Ref. [3] applied stationary wavelet entropy to extract features in the frequency domain followed by a single hidden layer feedforward neural ... networks in heatmaps ...