Название: TensorFlow Developer Certification Guide: Crack Google’s official exam on getting skilled with managing production-grade ML models
Автор: Patrick J
Издательство: GitforGits
Год: 2023
Страниц: 212
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB

Designed with both beginners and professionals in mind, the book is meticulously structured to cover a broad spectrum of concepts, applications, and hands-on practices that form the core of the TensorFlow Developer Certificate exam. Starting with foundational concepts, the book guides you through the fundamental aspects of TensorFlow, Machine Learning algorithms, and Deep Learning models. The initial chapters focus on data preprocessing, exploratory analysis, and essential tools required for building robust models. The book then delves into Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and advanced neural network techniques such as GANs and Transformer Architecture. Emphasizing practical application, each chapter is peppered with detailed explanations, code snippets, and real-world examples, allowing you to apply the concepts in various domains such as text classification, sentiment analysis, object detection, and more. A distinctive feature of the book is its focus on various optimization and regularization techniques that enhance model performance. As the book progresses, it navigates through the complexities of deploying TensorFlow models into production. It includes exhaustive sections on TensorFlow Serving, Kubernetes Cluster, and edge computing with TensorFlow Lite. The book provides practical insights into monitoring, updating, and handling possible errors in production, ensuring a smooth transition from development to deployment. The final chapters are devoted to preparing you for the TensorFlow Developer Certificate exam. From strategies, tips, and coding challenges to a summary of the entire learning journey, these sections serve as a robust toolkit for exam readiness. With hints and solutions provided for challenges, you can assess your knowledge and fine-tune your problem solving skills.

Название: The Python For Finance Handbook: Unlocking Financial Insights with Python
Автор: Hayden Van Der Post, Vincent Bisette
Издательство: Reactive Publishing
Год: 2024
Страниц: 583
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB

Dive into the dynamic world of finance armed with the power of Python! In 'The Python for Finance Handbook,' embark on a journey where financial expertise meets cutting-edge technology. This comprehensive guide equips you with the essential tools and techniques to leverage Python programming for financial analysis, modeling, and decision-making. From mastering data manipulation to implementing advanced statistical methods, this handbook demystifies complex financial concepts through practical examples and real-world applications. Whether you're a seasoned finance professional or a newcomer to the field, this book empowers you to harness Python's versatility to streamline processes, gain insights, and make informed investment decisions. Explore key topics such as portfolio optimization, risk management, algorithmic trading, and more, as you discover how Python revolutionizes traditional finance practices. With step-by-step instructions, code snippets, and hands-on exercises, 'The Python for Finance Handbook' is your indispensable companion for navigating the intersection of finance and technology. Elevate your financial prowess and stay ahead in today's rapidly evolving financial landscape with Python as your guide. Python's simplicity and readability have made it particularly attractive for finance professionals who are not primarily programmers but require a tool that can extend their analytical capabilities without a steep learning curve. The language's syntax is intuitive and emphasizes readability, which facilitates easier maintenance and collaboration among professionals from various disciplines.

Название: Software Engineering and Management: Theory and Application: Volume 16
Автор: Roger Lee
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2024
Страниц: 238
Язык: английский
Формат: pdf (true), epub
Размер: 31.9 MB

The book reports state of the art results in Software Engineering Research, Management & Applications in both printed and electronic form. SCI (Studies in Computation Intelligence) has grown into the most comprehensive computational intelligence research forum available in the world. Federated Leaning (FL), as a distributed Machine Learning paradigm, enables the collaboration of a group of agents to collaboratively conduct a learning model training, while preserving the data privacy. Federated Learning is viable distributed Machine Learning framework that can be applied to different smart-world systems, supported by the advancement of rapidly growing networking and computing techniques. Federated Learning (FL) has made possible the collaborative training of Machine Learning models between aggregation server and clients without sharing their private data. With the massive volume of heterogeneous data from various clients, the server faces challenges such as data unbalance, data corruption, and/or data irrelevancy. As a result, the FL setting is exposed to numerous security risks that lead to performance deterioration of learning effectiveness. To tackle the issue, in this paper we propose the Heterogeneity Index Based Clustering (HIC) approach, which enables the dynamic categorization of clients into clusters. Particularly, the model weights are dynamically clustered based on their heterogeneity level using an affinity propagation method. The HIC approach uses a simple, but effective way of scaling data heterogeneity and dynamic clustering to create a resilient learning system against backdoor attacks that outperforms the existing works on FL robustness.

Название: Exploration of Artificial Intelligence and Blockchain Technology in Smart and Secure Healthcare
Автор: Arvind K. Sharma, Dalip Kamboj, Savita Wadhawan, Gousia Habib
Издательство: Bentham Books
Серия: Advances in Computing Communications and Informatics
Год: 2024
Страниц: 316
Язык: английский
Формат: pdf (true), epub
Размер: 18.4 MB

This book offers in-depth reviews of different techniques and novel approaches of using blockchain and Artificial Intelligence (AI) in smart healthcare services. The volume brings 14 reviews and research articles written by academicians, researchers and industry professionals to give readers a current perspective of smart healthcare solutions for medical and public health services. From the last decade onward, a considerable amount of research and developments in technology have taken place especially in the healthcare industry, with the involvement of technologies like Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Blockchain, Communication Systems, Internet of Things, Multisensory Systems, etc. The purpose is only to make smart and secure healthcare possible. Machine Learning techniques and algorithms are required to give a boost to the aim of smart healthcare. Hence, to fulfill the vision of smart healthcare, the tools and applications based on Artificial Intelligence and Machine Learning are becoming extremely popular for producing more accuracy (in terms of values) in predicting results (without being explicitly programmed) in the healthcare industry. However, Artificial Intelligence-based algorithms are quite helpful for the transformation of physiological data into clinical information of real values, but for processing such big data from a set of medical images and identifying or extracting characteristic patterns of health function, then translating these patterns into clinical information definitely requires an adequate knowledge base of physiology, advanced digital signal processing capabilities, and Machine Learning.

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.