Advanced Analytics and Deep Learning Models. Группа авторов

Advanced Analytics and Deep Learning Models

href="#ulink_e2ddd03b-80e7-54e3-afc1-6dba213c4984">7.4 Denoising Autoencoder 7.5 Recursive Neural Network (RCNN) 7.6 Deep Reinforcement Learning 7.7 Deep Belief Networks (DBNS) 7.8 Conclusion References 8 A Proposed LSTM-Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG 8.1 Introduction 8.2 Background and Motivation 8.3 Related Work 8.4 Methodology of Proposed System 8.5 Results and Discussions 8.6 Conclusion References 9 An Extensive Survey of Applications of Advanced Deep Learning Algorithms on Detection of Neurodegenerative Diseases and the Tackling Procedure in Their Treatment Protocol 9.1 Introduction 9.2 Story of Alzheimer’s Disease 9.3 Datasets 9.4 Story of Parkinson’s Disease 9.5 A Review on Learning Algorithms 9.6 A Review on Methodologies 9.7 Results and Discussion 9.8 Conclusion References 10 Emerging Innovations in the Near Future Using Deep Learning Techniques 10.1 Introduction 10.2 Related Work 10.3 Motivation 10.4 Future With Deep Learning/Emerging Innovations in Near Future With Deep Learning 10.5 Open Issues and Future Research Directions 10.6 Deep Learning: Opportunities and Challenges 10.7 Argument with Machine Learning and Other Available Techniques 10.8 Conclusion With Future Work Acknowledgement References 11 Optimization Techniques in Deep Learning Scenarios: An Empirical Comparison 11.1 Introduction 11.2 Optimization and Role of Optimizer in DL 11.3 Various Optimizers in DL Practitioner Scenario 11.4 Recent Optimizers in the Pipeline 11.5 Experiment and Results 11.6 Discussion and Conclusion References