Handbook of Deep Learning Applications

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Balas, Valentina Emilia (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Roy, Sanjiban Sekhar (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Sharma, Dharmendra (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Samui, Pijush (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Smart Innovation, Systems and Technologies, 136
• Θέματα: Computational intelligence.; Artificial intelligence.; Signal processing.; Image processing.; Speech processing systems.; Neural networks (Computer science) .; Data mining.; Computational Intelligence.; Artificial Intelligence.; Signal, Image and Speech Processing.; Mathematical Models of Cognitive Processes and Neural Networks.; Data Mining and Knowledge Discovery.

 This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain-computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars. 

 Designing a Neural Network from scratch for Big Data powered by Multi-node GPUs -- Deep Learning for Scene Understanding -- Deep Learning for Driverless Vehicles -- Deep Learning for Document Representation -- Deep learning for marine species recognition -- Deep molecular representation in Cheminformatics -- Deep Learning in eHealth -- Deep Learning for Brain Computer Interfaces -- Deep Learning in Gene Expression Modeling.