This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods.
... based on LSTM stacked autoencoders for unsupervised detection of abnormal working conditions in semiconductor manufacturing ... anomaly detection approaches using LSTM and LSTM autoencoder techniques with the applications in supply chain ...
... approach to develop scalable all-optical multi-bit operations for high-speed data processing in Deep Learning ... based prediction system of distributed generation for the management of microgrids. IEEE Trans. Industry Appl. 55, 7092–7102 ...
... Anomaly detection in x-ray security imaging: a tensor-based learning approach, p. 1–8 (2021) 21. Obukhov, A ... semiconductor manufacturing. Expert Syst. Appl. 183, 115429 (2021) 25. Simonyan, K., Zisserman, A.: Very deep ...
With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.
This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.
... based method for classification of the wheat grains using artificial neural network ( 2017 ) 34. Xia , C. , Yang , S. , Huang , M. , et al .: Maize seed classification using hyperspectral image cou- pled with multi - linear discriminant ...
... learning method based on improved AdaBoost for rare class analysis , ” Journal of Computational Information Systems ... Scalable Random Forest Algorithm Based on MapReduce , ” IEEE , pp . 849–852 , 2013 . 32. D. Chen , Y. Yang ...
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book.
This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of "neuromorphic photonics.