Описание: Artificial intelligence is at the forefront of research and implementation in many industries including healthcare and agriculture. Whether it's detecting disease or generating algorithms, deep learning techniques are advancing exponentially. Researchers and professionals need a platform in which they can keep up with machine learning trends and their developments in the real world.
The Handbook of Research on Applications and Implementations of Machine Learning Techniques provides innovative insights into the multi-disciplinary applications of machine learning algorithms for data analytics. The content within this publication examines disease identification, neural networks, and language support. It is designed for IT professionals, developers, data analysts, technology specialists, R&D professionals, industrialists, practitioners, researchers, academicians, and students seeking research on deep learning procedures and their enactments in the fields of medicine, engineering, and computer science.
Автор: Bhattacharyya Siddhartha, Banerjee Pinaki, Majumdar Dipankar Название: Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications ISBN: 1466694742 ISBN-13(EAN): 9781466694743 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 41580.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Conventional computational methods, and even the latest soft computing paradigms, often fall short in their ability to offer solutions to many real-world problems due to uncertainty, imprecision, and circumstantial data. Hybrid intelligent computing is a paradigm that addresses these issues to a considerable extent.The Handbook of Research on Advanced Research on Hybrid Intelligent Techniques and Applications highlights the latest research on various issues relating to the hybridization of artificial intelligence, practical applications, and best methods for implementation. Focusing on key interdisciplinary computational intelligence research dealing with soft computing techniques, pattern mining, data analysis, and computer vision, this book is relevant to the research needs of academics, IT specialists, and graduate-level students.
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
Описание: This book discusses topics related to bioinformatics, statistics, and machine learning, presenting the latest research in various areas of bioinformatics.
Описание: Statistical Model Checking: Past, Present and Future.- Hypothesis testing for rare-event simulation: limitations and possibilities.- Survey of Statistical Verification of Linear Unbounded Properties: Model Checking and Distances.- Feedback Control for Statistical Model Checking of Cyber-Physical Systems.- Probabilistic Model Checking of Incomplete Models.- Plasma Lab: A Modular Statistical Model Checking Platform.- Synthesizing Energy-Optimal Controllers for Multiprocessor Dataflow Applications with UPPAAL STRATEGO.- Statistical Model Checking for Product Lines.- Towards Adaptive Scheduling of Maintenance for Cyber-Physical Systems.- Better railway engineering through statistical model checking.- On Creation and Analysis of Reliability Models by Means of Stochastic Timed Automata and Statistical Model Checking: Principle.- Automatic Synthesis of Code using Genetic Programming.- Evaluation and Reproducibility of Program Analysis and Verification (Track Introduction).- Symbolic Execution with CEGAR.- Multi-Core Model Checking of Large-Scale Reactive Systems Using Different State Representations.- Sparse Analysis of Variable Path Predicates Based Upon SSA-Form.- A Model Interpreter for Timed Automata.- ModSyn-PP: Modular Synthesis of Programs and Processes: Track Introduction.- Combinatory Process Synthesis.- Synthesis from a Practical Perspective.- A Long and Winding Road Towards Modular Synthesis.- Semantic heterogeneity in the formal development of complex systems: an introduction.- On the Use of Domain and System Knowledge Modeling in Goal-Based Event-B Specifications.- Strengthening MDE and Formal Design Models by references to Domain Ontologies. A Model Annotation Based Approach.- Towards Functional Requirements Analytics.- Heterogeneous Semantics and Unifying Theories.- Static and Runtime Verification: Competitors or Friends?.- StaRVOOrS - Episode II, Strengthen and Distribute the Force.- A Model-Based Approach to Combining Static and Dynamic Verification Techniques.- Information flow analysis for Go.- Challenges in High-Assurance Runtime Verification .- Static versus Dynamic Verification in Why3, Frama-C and SPARK 2014.- Considering Type-State Verification for Quantified Event Automata.- Combining Static and Runtime Methods to Achieve Safe Standing-Up for Humanoid Robots.- On Combinations of Static and Dynamic Analysis.- Safer Refactorings.- Rigorous Engineering of Collective Adaptive Systems.- Programming of CAS systems by relying on attribute-based communication.- Towards Static Analysis of Policy-Based Self-Adaptive Computing Systems.- A Calculus for Open Ensembles and Their Composition.- Logic Fragments: coordinating entities with logic programs.- Mixed-Critical Systems Design with Coarse-grained Multi-core Interference.- A Library and Scripting Language for Tool Independent Simulation Descriptions.- Adaptation to the unforeseen: Do we master our autonomous systems?'-- Questions to the Panel.- Smart coordination of autonomic component ensembles in the context of ad-hoc communication.- A Tool-chain for Statistical Spatio-Temporal Model Checking of Bike-sharing Systems.- Rigorous graphical modelling of movement in Collective Adaptive Systems.- Integration and Promotion of Autonomy with the ARE Framework.- Safe Artificial Intelligence and Formal Methods.- Engineering Adaptivity, Universal Autonomous Systems, Ethics and Compliance Issues.- Correctness-by-Construction and Post-hoc Verification: Friends or Foes?.- Correctness-by-Construction and Post-hoc Verification: A Marriage of Convenience?.- Deductive Verification of Legacy Code.- Correctness-by-Construction $\land$ Taxonomies $\Rightarrow$\\ Deep Comprehension of Algorithm Families.- Conditions for Compatibility of Components - The case of masters and slaves.- A Logic for Statistical Model Checking of Dynamic Software Architectures.- On two Friends for getting Correct Programs - Automatically Translating Event-B Specifications to Recursive Algorithms
Описание: The two-volume set LNCS 8802 and LNCS 8803 constitutes the refereed proceedings of the 6th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation, ISoLA 2014, held in Imperial, Corfu, Greece, in October 2014.
Описание: Covers development in the field of automated deduction. This book focuses on the investigation of problems derived from realistic applications. It focuses on basic research in deduction and on the knowledge on which modern deductive systems are based. It presents techniques of implementation and details about system building.
Описание: Presents the concepts and methods in automated deduction. This work focuses on basic research in deduction and on the knowledge on which modern deductive systems are based. It presents techniques of implementation and details about system building. It deals with applications of deductive techniques.
Описание: Features the concepts and methods in automated deduction. This work focuses on basic research in deduction and on the knowledge on which modern deductive systems are based. It presents techniques of implementation and details about system building. It deals with applications of deductive techniques.
Описание: BASIC CONCEPTS OF INTERACTIVE THEOREM PROVING Interactive Theorem Proving ultimately aims at the construction of powerful reasoning tools that let us (computer scientists) prove things we cannot prove without the tools, and the tools cannot prove without us.
Описание: rather the books present the concepts and methods now available in automated deduction in a form which can be easily accessed by scientists working in applications outside of the field of deduction.
Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with Python
Key Features
Understand how to obtain financial data via Quandl or internal systems
Automate commercial banking using artificial intelligence and Python programs
Implement various artificial intelligence models to make personal banking easy
Book Description
Remodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI). Hands-On Artificial Intelligence for Banking is a practical guide that will help you advance in your career in the banking domain. The book will demonstrate AI implementation to make your banking services smoother, more cost-efficient, and accessible to clients, focusing on both the client- and server-side uses of AI.
You'll begin by understanding the importance of artificial intelligence, while also gaining insights into the recent AI revolution in the banking industry. Next, you'll get hands-on machine learning experience, exploring how to use time series analysis and reinforcement learning to automate client procurements and banking and finance decisions. After this, you'll progress to learning about mechanizing capital market decisions, using automated portfolio management systems and predicting the future of investment banking. In addition to this, you'll explore concepts such as building personal wealth advisors and mass customization of client lifetime wealth. Finally, you'll get to grips with some real-world AI considerations in the field of banking.
By the end of this book, you'll be equipped with the skills you need to navigate the finance domain by leveraging the power of AI.
What you will learn
Automate commercial bank pricing with reinforcement learning
Perform technical analysis using convolutional layers in Keras
Use natural language processing (NLP) for predicting market responses and visualizing them using graph databases
Deploy a robot advisor to manage your personal finances via Open Bank API
Sense market needs using sentiment analysis for algorithmic marketing
Explore AI adoption in banking using practical examples
Understand how to obtain financial data from commercial, open, and internal sources
Who this book is for
This is one of the most useful artificial intelligence books for machine learning engineers, data engineers, and data scientists working in the finance industry who are looking to implement AI in their business applications. The book will also help entrepreneurs, venture capitalists, investment bankers, and wealth managers who want to understand the importance of AI in finance and banking and how it can help them solve different problems related to these domains. Prior experience in the financial markets or banking domain, and working knowledge of the Python programming language are a must.
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