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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.
How can we select the best performing data-driven model? How can we
rigorously estimate its generalization error? Statistical learning
theory answers these questions by deriving non-asymptotic bounds on
the generalization error of a model or, in other words, by upper
bounding the true error of the learned model based just on
quantities computed on the available data. However, for a long
time, Statistical learning theory has been considered only an
abstract theoretical framework, useful for inspiring new learning
approaches, but with limited applicability to practical problems.
The purpose of this book is to give an intelligible overview of the
problems of model selection and error estimation, by focusing on
the ideas behind the different statistical learning theory
approaches and simplifying most of the technical aspects with the
purpose of making them more accessible and usable in practice. The
book starts by presenting the seminal works of the 80's and
includes the most recent results. It discusses open problems and
outlines future directions for research.
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 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 is intended for a first course on microprocessor-based
systems design for engineering and computer science students. It
starts with an introduction of the fundamental concepts, followed
by a practical path that guides readers to developing a basic
microprocessor example, using a step-by-step problem-solving
approach. Then, a second microprocessor is presented, and readers
are guided to the implementation and programming of microcomputer
systems based on it. The numerous worked examples and solved
exercises allow a better understanding and a more effective
learning. All the examples and exercises were developed on Deeds
(Digital Electronics Education and Design Suite), which is freely
available online on a website developed and maintained by the
authors. The discussed examples can be simulated by using Deeds and
the solutions to all exercises and examples can be found on that
website. Further, in the last part of this book, different
microprocessor-based systems, which have been specifically thought
for educational purposes, are extensively developed, simulated and
implemented on FPGA-based platforms. This textbook draws on the
authors' extensive experience in teaching and developing learning
materials for bachelor's and master's engineering courses. It can
be used for self-study as well, and even independently from the
simulator. Thanks to the learning-by-doing approach and the
plentiful examples, no prior knowledge in computer programming is
required.
This book is intended for a first course on microprocessor-based
systems design for engineering and computer science students. It
starts with an introduction of the fundamental concepts, followed
by a practical path that guides readers to developing a basic
microprocessor example, using a step-by-step problem-solving
approach. Then, a second microprocessor is presented, and readers
are guided to the implementation and programming of microcomputer
systems based on it. The numerous worked examples and solved
exercises allow a better understanding and a more effective
learning. All the examples and exercises were developed on Deeds
(Digital Electronics Education and Design Suite), which is freely
available online on a website developed and maintained by the
authors. The discussed examples can be simulated by using Deeds and
the solutions to all exercises and examples can be found on that
website. Further, in the last part of this book, different
microprocessor-based systems, which have been specifically thought
for educational purposes, are extensively developed, simulated and
implemented on FPGA-based platforms. This textbook draws on the
authors' extensive experience in teaching and developing learning
materials for bachelor's and master's engineering courses. It can
be used for self-study as well, and even independently from the
simulator. Thanks to the learning-by-doing approach and the
plentiful examples, no prior knowledge in computer programming is
required.
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 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.
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