Название: Deep Learning Tools for Predicting Stock Market Movements
Автор: Renuka Sharma, Kiran Mehta
Издательство: Wiley-Scrivener
Год: 2024
Страниц: 489
Язык: английский
Формат: pdf (true), epub
Размер: 14,5 MB

The book provides a comprehensive overview of current research and developments in the field of Deep Learning models for stock market forecasting in the developed and developing worlds. The book delves into the realm of Deep Learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep Learning helps foresee market trends with increased accuracy. With advancements in Deep Learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis. The present study gives a comprehensive overview of the advancements and potential avenues in the realm of utilizing Deep Learning techniques for forecasting stock market trends. This study surveys the evolving landscape of Deep Learning methodologies employed in predicting stock price movements and offers insights into their effectiveness across various time frames and market conditions. The research delves into the multifaceted aspects of this field, encompassing architectures such as recurrent neural networks (RNNs), convolutional neural networks (CNNs), and more recent transformer-based models.

Название: Dependable Computing: Design and Assessment
Автор: Ravishankar K. Iyer, Zbigniew T. Kalbarczyk, Nithin M. Nakka
Издательство: Wiley-IEEE Press
Год: 2024
Страниц: 851
Язык: английский
Формат: pdf (true), epub
Размер: 24.5 MB

Covering dependability from software and hardware perspectives. Dependable Computing: Design and Assessment looks at both the software and hardware aspects of dependability. This book is suitable for those studying in the fields of computer engineering and Computer Science. Professionals who are working within the new reality to ensure dependable computing will find helpful information to support their efforts. With the support of practical case studies and use cases from both academia and real-world deployments, the book provides a journey of developments that include the impact of Artificial Intelligence and Machine Learning on this ever-growing field. This book offers a single compendium that spans the myriad areas in which dependability has been applied, providing theoretical concepts and applied knowledge with content that will excite a beginner, and rigor that will satisfy an expert. Accompanying the book is an online repository of problem sets and solutions, as well as slides for instructors, that span the chapters of the book.

Название: Artificial Intelligence for Learning: Using AI and Generative AI to Support Learner Development, 2nd Edition
Автор: Donald Clark
Издательство: Kogan Page
Год: 2024
Страниц: 313
Язык: английский
Формат: pdf (true), epub
Размер: 22.7 MB

With Artificial Intelligence (AI) creating huge opportunities for learning and employee development, how can learning professionals best implement the use of AI into their environment? Artificial Intelligence for Learning is the essential guide for learning professionals who want to understand how to use AI to improve all aspects of learning in organizations. This new edition debunks the myths and misconceptions around AI, discusses the learning theory behind generative AI and gives strategic and practical advice on how AI can be used. This book also includes specific guidance on how AI can provide learning support, chatbot functionality and content, as well as ideas on ethics and personalization. This book is necessary reading for all learning practitioners needing to understand AI and what it means in practice. AI in one sense means doing what humans do when they learn. The AI field is thick with references to ‘learning’, the most common being machine, deep and reinforcement learning. Machine Learning uses algorithms and statistical models and applies patterns and inferences to perform tasks through experience. This is analogous to traditional learning through exposure to taught experiences. Deep Learning is a Machine Learning technique that uses layered neural networks with large datasets, supervised, semi-supervised or unsupervised, to perform tasks. This is more like learning from the real world to gain competence and solve actual problems. Reinforcement learning operates on maximizing reward by exposure to existing knowledge and exploring new knowledge.

Название: Computability and Complexity: Foundations and Tools for Pursuing Scientific Applications
Автор: Rod Downey
Издательство: Springer
Серия: Undergraduate Topics in Computer Science
Год: 2024
Страниц: 361
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

This is a book about computation, something which is ubiquitous in the modern world. More precisely, it examines computability theory and computational complexity theory. Computability theory is the part of mathematics and Computer Science which seeks to clarify what we mean by computation or algorithm. When is there a computational solution possible to some question? How can we show that none is possible? How computationally hard is the question we are concerned with? Arguably, this area lead to the development of digital computers. (Computational) complexity theory is an intellectual heir of computability theory. Complexity theory is concerned with understanding what resources are needed for computation, where typically we would measure the resources in terms of time and space. Can we perform some task in a feasible number of steps? Can we perform some algorithm with only a limited memory? Does randomness help? Are there standard approaches to overcoming computational difficulty?

Название: MATLAB and Simulink in Action: Programming, Scientific Computing and Simulation
Автор: Xue Dingyü, Pan Feng
Издательство: Springer
Год: 2024
Страниц: 472
Язык: английский
Формат: pdf (true), epub
Размер: 118.0 MB

The textbook is intended for teaching MATLAB language and its applications. MATLAB language is the most widely used computer languages in scientific computing, automatic control and system simulation. The book is composed of three parts: MATLAB programming, scientific computing with MATLAB, and system simulation with Simulink. Since MATLAB is widely used in all fields of science and engineering, a good introduction to the language can not only help students learn how to use it to solve practical problems, but also provide them with the skills to use MATLAB independently in their later courses and research. The three parts of the book are well-balanced and tailored to the needs of engineering students, and the mathematical problems commonly encountered in engineering can be easily solved using MATLAB. This textbook is suitable for undergraduate and graduate students majoring in science and engineering. MATLAB language is a highly integrated language. The statements are concise. It comes with powerful built-in facilities, such that tens or hundreds lines of source code in ordinary C or Fortran can be solved with a couple of lines in MATLAB. It is highly reliable and easy to maintain. The efficiency in solving scientific problems is significantly promoted.

Название: A Practical Guide to Data Analysis Using R: An Example-Based Approach
Автор: John H. Maindonald, W. John Braun, Jeffrey L. Andrews
Издательство: Cambridge University Press
Год: 2024
Страниц: 551
Язык: английский
Формат: pdf
Размер: 15.9 MB

Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and Graphics Using R,' 3rd edition, it expands upon topics including cluster analysis, exponential time series, matching, seasonality, and resampling approaches. An extended look at p-values leads to an exploration of replicability issues and of contexts where numerous p-values exist, including gene expression. Developing practical intuition, this book assists scientists in the analysis of their own data, and familiarizes students in statistical theory with practical data analysis. The worked examples and accompanying commentary teach readers to recognize when a method works and, more importantly, when it doesn't. Each chapter contains copious exercises. Selected solutions, notes, slides, and R code are available online, with extensive references pointing to detailed guides to R. This text is designed as an aid, for learning and for reference, in the navigation of a world in which unprecedented new data sources, and tools for data analysis, are pervasive. It aims to teach, using real-world examples, a style of analysis and critique that, given meaningful data, can generate defensible analysis results. The text is suitable for a style of learning where readers work through the text with a computer at their side, running the R code as and when this seems helpful. It complements more mathematically oriented accounts of statistical methodology. The appendix provides a brief account of R, primarily as a starting point for learning. We encourage readers with limited R experience to avail themselves of the wealth of instructional material on the web.

Название: The Mathematics of Machine Learning: Lectures on Supervised Methods and Beyond
Автор: Maria Han Veiga, François Gaston Ged
Издательство: De Gruyter
Год: 2024
Страниц: 210
Язык: английский
Формат: pdf (true), epub
Размер: 29.0 MB

This book is an introduction to Machine Learning, with a strong focus on the mathematics behind the standard algorithms and techniques in the field, aimed at senior undergraduates and early graduate students of Mathematics. There is a focus on well-known Supervised Machine Learning algorithms, detailing the existing theory to provide some theoretical guarantees, featuring intuitive proofs and exposition of the material in a concise and precise manner. A broad set of topics is covered, giving an overview of the field. A summary of the topics covered is: statistical learning theory, approximation theory, linear models, kernel methods, Gaussian processes, deep neural networks, ensemble methods and unsupervised learning techniques, such as clustering and dimensionality reduction. Machine Learning aims at building algorithms that autonomously learn how to perform a task from examples. This definition is rather vague on purpose, but to make it slightly clearer, by “autonomously” we mean that no expert is teaching (or coding by hand) the solution; by “learn” we mean that we have a measure of performance of the algorithm output on the task. This book is suited for students who are interested in entering the field, by preparing them to master the standard tools in Machine Learning. The reader will be equipped to understand the main theoretical questions of the current research and to engage with the field.

Название: Uncertainty Quantification with R: Bayesian Methods
Автор: Eduardo Souza de Cursi
Издательство: Springer
Год: 2024
Страниц: 493
Язык: английский
Формат: pdf (true)
Размер: 17.4 MB

This book is a rigorous but practical presentation of the Bayesian techniques of uncertainty quantification, with applications in R. This volume includes mathematical arguments at the level necessary to make the presentation rigorous and the assumptions clearly established, while maintaining a focus on practical applications of Bayesian uncertainty quantification methods. Practical aspects of applied probability are also discussed, making the content accessible to students. The introduction of R allows the reader to solve more complex problems involving a more significant number of variables. Users will be able to use examples laid out in the text to solve medium-sized problems. The list of topics covered in this volume includes basic Bayesian probabilities, entropy, Bayesian estimation and decision, sequential Bayesian estimation, and numerical methods. Blending theoretical rigor and practical applications, this volume will be of interest to professionals, researchers, graduate and undergraduate students interested in the use of Bayesian uncertainty quantification techniques within the framework of operations research and mathematical programming, for applications in management and planning. This book targets the use of R, which is a GNU project to develop a tool for language and environment for statistical computing and graphics. An IDE is proposed by RStudio. The popularity of R and RStudio make that the reader will find on the web many sites and information about it. A wide literature can also be found about this software. The community of the users of R proposes a large choice of packages to extend the possibilities of R.

Название: Ultimate Machine Learning with Scikit-Learn: Unleash the Power of Scikit-Learn and Python to Build Cutting-Edge Predictive Modeling Applications and Unlock Deeper Insights Into Machine Learning
Автор: Parag Saxena
Издательство: Orange Education Pvt Ltd, AVA
Год: 2024
Страниц: 411
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB

Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn. “Ultimate Machine Learning with Scikit-Learn” is a definitive resource that offers an in-depth exploration of data preparation, modeling techniques, and the theoretical foundations behind powerful machine learning algorithms using Python and Scikit-Learn. Beginning with foundational techniques, you'll dive into essential skills for effective data preprocessing, setting the stage for robust analysis. Next, logistic regression and decision trees equip you with the tools to delve deeper into predictive modeling, ensuring a solid understanding of fundamental methodologies. You will master time series data analysis, followed by effective strategies for handling unstructured data using techniques like Naive Bayes. Transitioning into real-time data streams, you'll discover dynamic approaches with K-nearest neighbors for high-dimensional data analysis with Support Vector Machines (SVMs). Alongside, you will learn to safeguard your analyses against anomalies with isolation forests and harness the predictive power of ensemble methods, in the domain of stock market data analysis. By the end of the book you will master the art of data engineering and ML pipelines, ensuring you're equipped to tackle even the most complex analytics tasks with confidence.

Название: Лямбда-выражения в Java 8. Функциональное программирование – в массы
Автор: Уорбэртон Р.
Издательство: Москва
Год: 2014
Cтраниц: 192
Формат: pdf (ocr)
Размер: 21 мб
Язык: русский

Если вы имеете опыт работы с Java SE, то из этой книги узнаете об изменениях в версии Java 8, обусловленных появлением в языке лямбда-выражений. Вашему вниманию будут представлены примеры кода, упражнения и увлекательные объяснения того, как можно использовать эти анонимные функции, чтобы сделать код проще и чище, и как библиотеки помогают в решении прикладных задач.