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Applied Deep Learning with TensorFlow 2 : Learn to Implement Advanced Deep Learning Techniques with Python
Focuses on the fundamental concepts and at the same time on practical aspects of implementing neural networks and deep learning for your research projects. This book is designed so that you can focus on the parts you are interested in. You will explore topics as regularization, optimizers, optimization, metric analysis, and hyper-parameter tuning. In addition, you will learn the fundamentals ideas behind autoencoders and generative adversarial networks. All the code presented in the book will be available in the form of Jupyter notebooks which would allow you to try out all examples and extend them in interesting ways. A companion online book is available with the complete code for all examples discussed in the book and additional material more related to TensorFlow and Keras. All the code will be available in Jupyter notebook format and can be opened directly in Google Colab (no need to install anything locally) or downloaded on your own machine and tested locally. You will: Understand the fundamental concepts of how neural networks work / Learn the fundamental ideas behind autoencoders and generative adversarial networks / Be able to try all the examples with complete code examples that you can expand for your own projects / Have available a complete online companion book with examples and tutorials.
Applied and computational mathematics for digital environments
Contains the 11 papers that were accepted and published in the Special Issue “Applied and Computational Mathematics for Digital Environments” of the MDPI Mathematics journal. The topics of interest include, among others, scientific research, applied tasks, and problems in the following areas: The construction of mathematical and information models of intelligent computer systems for monitoring and controlling the parameters of digital environments; The development of intelligent optimization algorithms that search for optimal parameter values of mathematical and information models in digital environments; Software and mathematical technologies in the implementation of intelligent monitoring and computer control of the parameters of digital environments; The development and application of mathematical and information models, machine learning methods, and artificial intelligence for the analysis and processing of big data in digital environments.
Applications of computational intelligence
Computational intelligence (CI) is the theory, design, application, and development of biologically and linguistically motivated computational paradigms. Traditionally, the three main pillars of CI have been neural networks, fuzzy systems, and evolutionary computation. However, in time, many nature-inspired computing paradigms have evolved. Thus, CI is an evolving field, and, at present, in addition to the three main constituents, it encompasses computing paradigms such as ambient intelligence, artificial life, cultural learning, artificial endocrine networks, social reasoning, and artificial hormone networks. CI plays a major role in developing successful intelligent systems, including games and cognitive developmental systems.
Application and theory of multimedia signal processing using machine learning or advanced methods
Consists of a collection of peer-reviewed published papers on various advanced technology researches related to signal processing applications and theories for multimedia systems using machine learning or advanced methods. Multimedia signals include image, video, audio, character recognition, and communication channel optimization for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition.
Anticipatory Behavior in Adaptive Learning Systems : From Brains to Individual and Social Behavior
Anticipatory behavior in adaptive learning systems is steadily gaining the - terest of scientists, although many researchers still do not explicitly consider the actual anticipatory capabilities of their systems.The introductory chapter of this volume therefore does not only provide an overview of the contributions included in this volume but also proposes a taxonomy of how anticipatory mechanisms can improve adaptive behavior and learning in cognitive systems. During the workshop it became clear that ant- ipations are involved in various cognitive processes that range from individual anticipatory mechanisms to social anticipatory behavior.
Anomaly Detection : Techniques and Applications
When information in the data warehouse is processed, it follows a definite pattern. An unexpected deviation in the data pattern from the usual behavior is called an anomaly. The anomaly in the data is also referred to as noise, outlier, spammer, deviations, novelties and exceptions. Identification of the rare items, events, observations, patterns which raise suspension by differing significantly from the majority of data is called anomaly detection. With progress in the technologies and the widespread use of data for the purpose for business the increase in the spams faced by the individuals and the companies are increasing day by day. This noisy data has boomed as a major problem in various areas such as Internet of Things, web service, Machine Learning, Artificial Intelligence, Deep learning, Image Processing, Cloud Computing, Audio processing, Video Processing, VoIP, Data Science, Wireless Sensor etc. Identifying the anomaly data and filtering them before processing is a major challenge for the data analyst. This anomaly is unavoidable in all areas of research. This book covers the techniques and algorithms for detecting the deviated data.
Analysis and Modelling of Faces and Gestures ; 2nd International workshop, AMFG 2005, Beijing, China, October 16, 2005, Proceedings
During the last 30 years, face recognition and related problems such as face detection/tracking and facial expression recognition have attracted researchers from both the engineering and psychology communities. In addition, extensive research has been carried out to study hand and body gestures. The understanding of how humans perceive these important cues has significant scientific value and extensive applications. this one-day workshop (AMFG 2005) provided a focused international forum to bring together well-known researchers and research groups to review the status of recognition, analysis and modeling of faces and gestures, to discuss the challenges that we are facing, and to explore future directions. Overall, 30 papers were selected from 90 submitted manuscripts. The topics of these papers range from feature representation, robust recognition, learning, and 3D modeling to psychology.
An Intuitive Exploration of Artificial Intelligence : Theory and Applications of Deep Learning
This book develops a conceptual understanding of Artificial Intelligence (AI), Deep Learning and Machine Learning in the truest sense of the word. It is an earnest endeavor to unravel what is happening at the algorithmic level, to grasp how applications are being built and to show the long adventurous road in the future.
An Introduction to Kolmogorov Complexity and Its Applications
Written by two experts in the field, this book is ideal for advanced undergraduate students, graduate students, and researchers in all fields of science. It is self-contained: it contains the basic requirements from mathematics, probability theory, statistics, information theory, and computer science. Included are history, theory, new developments, a wide range of applications, numerous (new) problem sets, comments, source references, and hints to solutions of problems. This is the only comprehensive treatment of the central ideas of Kolmogorov complexity and their applications.
An Introduction to Formal Languages and Automata
Designed for an introductory course on formal languages, automata, compatibility, and related matters forming what is known as the theory of computation
Algorithms for a New World : When Big Data and Mathematical Models Meet
Algorithms, artificial neural networks, and machine learning help us discover the opportunities and pitfalls of a world governed by mathematics and artificial intelligence.
Algorithms – ESA 2005 ; 13th Annual European Symposium, Palma de Mallorca, Spain, October 3-6, 2005, Proceedings
This volume contains the 75 contributed papers and the abstracts of the threeinvited lectures presented at the 13th Annual European Symposium on Algo-rithms (ESA 2005), held in Spain, 2005. respectively.Papers were solicited in all areas of algorithmic research, including but notlimited to algorithmic aspects of networks, approximation and on-line algo-rithms, computational biology, computational geometry, computational financeand algorithmic game theory, data structures, database and information re-trieval, external memory algorithms, graph algorithms, graph drawing, machinelearning, mobile computing, pattern matching and data compression, quantumcomputing, and randomized algorithms. The algorithms could be sequential,distributed, or parallel. Submissions were especially encouraged in the area ofmathematical programming and operations research, including combinatorialoptimization, integer programming, polyhedral combinatorics, and semidefiniteprogramming.Each extended abstract was submitted to one of the two tracks.
Algorithmic learning theory ; Vol. 3734 ; 16th international conference, ALT 2005, Singapore, October 8-11, 2005, Proceedings
This volume contains the papers presented at the 16th Annual InternationalConference on Algorithmic Learning Theory (ALT 2005), which was held (Republic of Singapore), 2005. The main objective of theconference is to provide an interdisciplinary forum for the discussion of the the-oretical foundations of machine learning as well as their relevance to practicalapplications. The volume includes 30 technical contributions, which were selected by theprogram committee from 98 submissions.
Algorithmic learning theory ; 18th International conference, ALT 2007, Sendai, Japan, October 1-4, 2007, Proceedings
This volume contains the papers presented at the 18th International Conf- ence on Algorithmic Learning Theory.The main objective of the conference was to provide an interdisciplinary forum for high-quality talks with a strong theore- cal background and scientiبهc interchange in areas such as query models, on-line learning, inductive inference, algorithmic forecasting, boosting, support vector machines, kernel methods, complexity and learning, reinforcement learning, - supervised learning and grammatical inference.
Algorithmic Learning in a Random World
This new monograph integrates mathematical theory and revealing experimental work. It demonstrates mathematically the validity of the reliability claimed by conformal predictors when they are applied to independent and identically distributed data, and it confirms experimentally that the accuracy is sufficient for many practical problems. Later chapters generalize these results to models called repetitive structures, which originate in the algorithmic theory of randomness and statistical physics. The approach is flexible enough to incorporate most existing methods of machine learning, including newer methods such as boosting and support vector machines and older methods such as nearest neighbors and the bootstrap.
AI-Enabled Threat Detection and Security Analysis for Industrial IoT
Provides the state-of-the-art development on security and privacy for cyber-physical systems (CPS) and industrial Internet of Things (IIoT). More specifically, this book discusses the security challenges in CPS and IIoT systems as well as how Artificial Intelligence (AI) and Machine Learning (ML) can be used to address these challenges. Furthermore, this book proposes various defence strategies, including intelligent cyber-attack and anomaly detection algorithms for different IIoT applications.
AI, data, and digitalization : First International Symposium, SAIDD 2023, Sogndal, Norway, May 9–10, 2023, Revised Selected Papers
Artificial Intelligence, Big Data and Digitalization allow us more than ever beforeto make use of the data our society and public and private sectors generate every day.Institutions around the world are increasingly turning to such methods and technologiesto help them solve complex problems, promote efficiency and improve performance anddecision-making.
AI in learning : Designing the future
AI (artificial intelligence) is predicted to radically change teaching and learning in both schools and industry causing radical disruption of work. AI can support well-being initiatives and lifelong learning but educational institutions and companies need to take the changing technology into account. Moving towards AI supported by digital tools requires a dramatic shift in the concept of learning, expertise and the businesses built off of it. Based on the latest research on AI and how it is changing learning and education, this book will focus on the enormous opportunities to expand educational settings with AI for learning in and beyond the traditional classroom. This book also introduces ethical challenges related to learning and education, while connecting human learning and machine learning.
AI in drug discovery
Constitutes the refereed proceedings of the First international workshop on ai in Drug Discovery, AIDD 2024, held as a part of the 33rd International Conference on Artificial Neural Networks, ICANN 2024, in Lugano, Switzerland, on September 19, 2024. These papers focus on various aspects of the rapidly evolving field of Artificial Intelligence (AI)-driven drug discovery in chemistry, including Big Data and advanced Machine Learning, eXplainable AI (XAI), Chemoinformatics, Use of deep learning to predict molecular properties, Modeling and prediction of chemical reaction data and Generative models.
AI For Emerging Verticals : Human-robot computing, sensing and networking
Artificial intelligence (AI) and machine learning (ML) will play a major role. By adopting AI software and services, businesses can create predictive strategies, enhance their capabilities, better interact with customers, and streamline their business processes. Explores novel concepts and cutting-edge research and developments towards designing these fully automated advanced digital systems. Fostered by technological advances in artificial intelligence and machine learning, such systems potentially have a wide range of applications in robotics, human computing, sensing and networking. The chapters focus on models and theoretical approaches to guarantee automation in large multi-scale implementations of AI and ML systems; protocol designs to ensure AI systems meet key requirements for future services such as latency; and optimisation algorithms to leverage the trusted distributed and efficient complex architectures.
