Deep neural evolution deep learning with evolutionary computation /: deep learning with evolutionary computation. (2020)

• Record Type: Book
• Further Information: Note: Hitoshi Iba, Nasimul Noman, editors.
• Other Names: Iba, Hitoshi; Noman, Nasimul
• Contents: Intro -- Preface -- Contents -- Abbreviations -- Part I Preliminaries -- 1 Evolutionary Computation and Meta-heuristics -- 1.1 Introduction -- 1.2 Evolutionary Algorithms: From Bullet Trains to Finance and Robots -- 1.3 Multi-Objective Optimization -- 1.4 Genetic Programming and Its Genome Representation -- 1.4.1 Tree-based Representation of Genetic Programming -- 1.4.2 Cartesian Genetic Programming (CGP) -- 1.5 Ant Colony Optimization (ACO) -- 1.6 Particle Swarm Optimization (PSO) -- 1.7 Artificial Bee Colony Optimization (ABC) -- 1.8 Firefly Algorithms -- 1.9 Cuckoo Search 1.10 Harmony Search (HS) -- 1.11 Conclusion -- References -- 2 A Shallow Introduction to Deep Neural Networks -- 2.1 Introduction -- 2.2 (Shallow) Neural Networks -- 2.2.1 Backpropagation Algorithm for Training NNs -- 2.3 Deep Neural Networks: What, Why and How? -- 2.4 Architectures of Deep Networks -- 2.4.1 Convolutional Neural Network -- 2.4.1.1 Convolutional Layers -- 2.4.1.2 Pooling Layers -- 2.4.1.3 Fully Connected Layers -- 2.4.1.4 Training Strategies -- 2.4.1.5 Popular CNN Models -- 2.4.2 Recurrent Neural Network -- 2.4.2.1 RNN Architecture -- 2.4.2.2 RNN Training -- 2.4.2.3 Memory Cells 2.4.3 Deep Autoencoder -- 2.4.4 Deep Belief Network (DBN) -- 2.4.5 Generative Adversarial Network (GAN) -- 2.4.5.1 GAN Architecture -- 2.4.5.2 GAN Training -- 2.4.5.3 Progresses in GAN Research -- 2.4.6 Recursive Neural Networks -- 2.5 Applications of Deep Learning -- 2.6 Conclusion -- References -- Part II Hyper-Parameter Optimization -- 3 On the Assessment of Nature-Inspired Meta-Heuristic Optimization Techniques to Fine-Tune Deep Belief Networks -- 3.1 Introduction -- 3.2 Theoretical Background -- 3.2.1 Restricted Boltzmann Machines -- 3.2.2 Contrastive Divergence 3.2.3 Persistent Contrastive Divergence -- 3.2.4 Deep Belief Networks -- 3.3 Meta-heuristic Optimization Algorithms -- 3.4 Methodology -- 3.4.1 Modeling DBN Hyper-parameter Fine-tuning -- 3.4.2 Datasets -- 3.4.3 Experimental Setup -- 3.5 Experimental Results -- 3.5.1 Training Evaluation -- 3.5.2 Time Analysis -- 3.5.3 Hyper-Parameters Analysis -- 3.6 Conclusions and Future Works -- References -- 4 Automated Development of DNN Based Spoken Language Systems Using Evolutionary Algorithms -- 4.1 Spoken Language Processing Systems -- 4.1.1 Principle of Speech Recognition 4.1.2 Hidden Markov Model Based Acoustic Modeling -- 4.1.3 End-to-End Speech Recognition System -- 4.1.4 Evaluation Measures -- 4.2 Evolutionary Algorithms -- 4.2.1 Genetic Algorithm -- 4.2.2 Evolution Strategy -- 4.2.3 Bayesian Optimization -- 4.3 Multi-Objective Optimization with Pareto Optimality -- 4.3.1 Pareto Optimality -- 4.3.2 CMA-ES with Pareto Optimality -- 4.3.3 Alternative Multi-Objective Methods -- 4.4 Experimental Setups -- 4.4.1 General Setups -- 4.4.2 Automatic Optimizations -- 4.5 Results -- 4.6 Conclusion -- References
• Publisher Details: Singapore : Springer
• Publication Date: 2020
• Extent: 1 online resource (437 p.)
• Subjects: 006.3/2; Neural networks (Computer science); Machine learning; Electronic books; Electronic books
• Languages: English
• ISBNs: 9789811536854; 9811536856
• Related ISBNs: 9789811536847; 9811536848
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