This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.

This book has been designed for a first course on digital design for engineering and computer science students. It offers an extensive introduction on fundamental theories, from Boolean algebra and binary arithmetic to sequential networks and finite state machines, together with the essential tools to design and simulate systems composed of a controller and a datapath. The numerous worked examples and solved exercises allow a better understanding and more effective learning. All of the examples and exercises can be run on the Deeds software, freely available online on a webpage developed and maintained by the authors. Thanks to the learning-by-doing approach and the plentiful examples, no prior knowledge in electronics of programming is required. Moreover, the book can be adapted to different level of education, with different targets and depth, be used for self-study, and even independently from the simulator. The book draws on the authors' extensive experience in teaching and developing learning materials.

This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of artificial neural networks, big data and related topics, organized by the International Neural Network Society and hosted by the University of Genoa. In 2019 INNS BDDL has been held in Sestri Levante (Italy) from April 16 to April 18. More than 80 researchers from 20 countries participated in the INNS BDDL in April 2019. In addition to regular sessions, INNS BDDL welcomed around 40 oral communications, 6 tutorials have been presented together with 4 invited plenary speakers. This book covers a broad range of topics in big data and deep learning, from theoretical aspects to state-of-the-art applications. This book is directed to both Ph.D. students and Researchers in the field in order to provide a general picture of the state-of-the-art on the topics addressed by the conference.

This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.

This book has been designed for a first course on digital design for engineering and computer science students. It offers an extensive introduction on fundamental theories, from Boolean algebra and binary arithmetic to sequential networks and finite state machines, together with the essential tools to design and simulate systems composed of a controller and a datapath. The numerous worked examples and solved exercises allow a better understanding and more effective learning. All of the examples and exercises can be run on the Deeds software, freely available online on a webpage developed and maintained by the authors. Thanks to the learning-by-doing approach and the plentiful examples, no prior knowledge in electronics of programming is required. Moreover, the book can be adapted to different level of education, with different targets and depth, be used for self-study, and even independently from the simulator. The book draws on the authors' extensive experience in teaching and developing learning materials.