Artificial Neural Networks The Brain Behind Ai
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Artificial Neural Networks: The brain behind AI
Neural networks are one of the most popular and powerful classes of machine learning algorithms. In quantitative finance neural networks are often used for time-series forecasting, constructing proprietary indicators, algorithmic trading, securities classification and credit risk modeling. They have also been used to construct stochastic process models and price derivatives. Despite their usefulness, neural networks tend to have a bad reputation because their performance is "temperamental". In my opinion, this can be attributed to poor network design owing to misconceptions regarding how neural networks work. This book discusses every aspect of the artificial neural network in very interactive, practical and simple way.
Artificial Neural Networks
As everyone knows Machine learning and AI are the things which everyone considers can change the world in every possible aspect. Artificial Neural Networks are most significant and important in AI and ML development. In this book, we are gonna see all basics of Artificial Neural Networks which will help you understand them better and will take you in this great world of AI development.
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence
Spiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.