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From the author of Million Dollar Maths comes a fascinating and engaging look at the mathematics that lies behind our modern world. How does Google know what you want to type? How did humans first reach the moon? Could we ever have a supercomputer the size of a pinhead? In this thrilling numerical journey, Hugh Barker explores how mathematics has helped to build the technology of today, and the fascinating ways it is shaping the future. From green energy to 3-D printing and from quantum cryptography to machine learning, Quantum Leaps reveals the hidden mathematics in modern technology. Beautifully written and full of ingenious examples, this book will make you realise how the modern world would be impossible without our formidable mathematical armoury.

Quantum Leaps

This book provides comprehensive coverage of combined Artificial Intelligence (AI) and Machine Learning (ML) theory and applications. Rather than looking at the field from only a theoretical or only a practical perspective, this book unifies both perspectives to give holistic understanding. The first part introduces the concepts of AI and ML and their origin and current state. The second and third parts delve into conceptual and theoretic aspects of static and dynamic ML techniques. The forth part describes the practical applications where presented techniques can be applied. The fifth part introduces the user to some of the implementation strategies for solving real life ML problems.  The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals. It makes minimal use of mathematics to make the topics more intuitive and accessible.   Presents a full reference to artificial intelligence and machine learning techniques - in 

Machine Learning and Artificial Intelligence

Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms.     The book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn’t required because complete examples are provided and explained.     Data Science Fundamentals with Python and MongoDB is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path

Data Science Fundamentals for Python and MongoDB

ENbook.pl

pstrongAre you ready to leverage the power of machine learning Well, this is the basic theory you need to go through first strongppstrongWhat if you could learn about machine learning without needing any coding experience or prior knowledgestrongppstrongImagine adding 'machine learning' to your LinkedIn profile and actually be able to use it to sort through data strongppstrongMulti-time best selling information technology and mathematics authorstrong, Edward Mize, presents the perfect introduction to machine learning for any beginner. With books and a blog that receives tens of thousands of readers monthly, Edward has compiled the most practical and high-level introduction to the components and statistical concepts found in machine learning. Well-known and incredibly popular for his ability to teach complex topics in an easy-to-understand way, you won't have any issues grasping these plain-English explanations.ppDo you want to learn about and implement machine learning but don't know w

Machine Learning | Mize Edward | Twarda

The edited book is a consolidated handbook on quantum computing that covers quantum basic science and mathematics to advanced concepts and applications of quantum computing and quantum machine learning applied to diverse domains.

Quantum Computing | Pandey Rajiv | Twarda

pbBehavior Analysis with Machine Learning Using R bintroduces machine learning and deep learning concepts and algorithms applied to a diverse set of behavior analysis problems. It focuses on the practical aspects of solving such problems based on data collected from sensors or stored in electronic records. The included examples demonstrate how to perform common data analysis tasks such as data exploration, visualization, preprocessing, data representation, model training and evaluation. All of this, using the R programming language and real-life behavioral data. Even though the examples focus on behavior analysis tasks, the covered underlying concepts and methods can be applied in any other domain. No prior knowledge in machine learning is assumed. Basic experience with R and basic knowledge in statistics and high school level mathematics are beneficial.ppFeatures pul p p liBuild supervised machine learningb bmodels to predict indoor locations based on WiFi signals, recognize physical 

Behavior Analysis with Machine Learning Using R | Ceja Enrique Garcia | Paperback | Twarda

pSignal processing captures, interprets, describes and manipulates physical phenomena. Mathematics, statistics, probability, and stochastic processes are among the signal processing languages we use to interpret real-world phenomena, model them, and extract useful information. This book presents different kinds of signals humans use and applies them for human machine interaction to communicate. ppiSignal Processing and Machine Learning with Applicationsi presents methods that are used to perform various Machine Learning and Artificial Intelligence tasks in conjunction with their applications. It is organized in three parts Realms of Signal Processing Machine Learning and Recognition and Advanced Applications and Artificial Intelligence. The comprehensive coverage is accompanied by numerous examples, questions with solutions, with historical notes. The book is intended for advanced undergraduate and postgraduate students, researchers and practitioners who are engaged with signal process

Signal Processing and Machine Learning with Applications | Richter Michael M. | Twarda | Twarda

Data-driven discovery is revolutionizing how we model, predict, and control complex systems. Now with Python and MATLABR, this textbook trains mathematical scientists and engineers for the next generation of scientific discovery by offering a broad overview of the growing intersection of data-driven methods, machine learning, applied optimization, and classical fields of engineering mathematics and mathematical physics. With a focus on integrating dynamical systems modeling and control with modern methods in applied machine learning, this text includes methods that were chosen for their relevance, simplicity, and generality. Topics range from introductory to research-level material, making it accessible to advanced undergraduate and beginning graduate students from the engineering and physical sciences. The second edition features new chapters on reinforcement learning and physics-informed machine learning, significant new sections throughout, and chapter exercises. Online supplementar

Data | Driven Science and Engineering | Brunton Steven L. | Twarda

The textbook provides students with tools they need to analyze complex data using methods from data science, machine learning and artificial intelligence. The authors include both the presentation of methods along with applications using the programming language R, which is the gold standard for analyzing data. The authors cover all three main components of data science computer science mathematics and statistics and domain knowledge. The book presents methods and implementations in R side-by-side, allowing the immediate practical application of the learning concepts. Furthermore, this teaches computational thinking in a natural way. The book includes exercises, case studies, QA and examples.pp

Elements of Data Science, Machine Learning, and Artificial Intelligence Using R | Emmert | Streib Frank | Twarda | Twarda

pUsing a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with Programming Tips that encourage the reader to write quality Python code. The entire text, including all the figures and numerical results, is reproducible using the Python codes provided, thus enabling readers to follow along by experimenting with the same code on their own computers.pp Modern Python modules like Pandas, Sympy, Scikit-learn, Statsmodels, Scipy, Xarray, Tensorflow, and Keras are used to implement and visualize important machine learning concepts like the biasvariance trade-off, cross-validation, interpretability, and re

Python for Probability, Statistics, and Machine Learning | Unpingco Jos | Twarda | Twarda

pIn this book, we work for the contents for knowledge transfer from the viewpoint of machine intelligence. We adopt the methodology from graphical theory, mathematical models, algorithmic implementation as well as datasets preparation, programming, results analysis and evaluations. We start from understanding artificial neural networks with neurons and the activation functions, then explain the mechanism of deep learning using advanced mathematics. We especially emphasize on how to use TensorFlow and the latest MATLAB deep learning toolboxes for implementing deep learning algorithms.ppBefore reading this book, we strongly encourage our readers to understand the knowledge of mathematics, especially those subjects like mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, information theory as well as basic algebra, functional analysis, graphical models, etc. The computational knowledge will assist us not only in understanding this boo

Computational Methods for Deep Learning | Yan Wei Qi | Twarda

pstrongSimple concepts about up-and-coming STEM science, technology, engineering, and mathematics to kick-start your future genius strongppMachine Learning for Babies  Toddlers is a great way to introduce basic concepts about machine learning, an exploding field that will be like electricity to our future generation. ppFamiliarize your little one with what machines are and they can learn, just like us ppThe colorful, beautiful, and visually stimulating illustrations and easy to understand text will encourage the child's sense of wonder, adventure, and curiosity and might stimulate your senses too ppstrongTwo levels of learning for your growing child strongp ul liLevel 1 Baby basics in black textli liLevel 2 Toddlers  beyond in purple textli ul pstrongGreat for homeschooling, preschools, or primary education with inviting font - large, clear, and color-coordinated for easy digestion of materialstrongp pstrongLook for all the books in the Tinker Toddler series strongp ul liThe Solar Syst

Machine Learning for Kids | Dhoot | Twarda

This book discusses the use of advanced techniques to produce and understand music in a digital way. It gathers the first-ever English-language proceedings of the Conference on Sound and Music Technology CSMT, which was held in Xiamen, China in 2018. As a leading event, the CSMT reflects the latest advances in acoustic and music technologies in China. Sound and technology are more closely linked than most people assume. For example, signal-processing methods form the basis of music feature extraction, while mathematics provides an objective means of representing current musicological theories and discovering new ones. Moreover, machine-learning methods include popular deep learning algorithms and are used in a broad range of contexts, from discovering patterns in music features to producing music. As these proceedings demonstrate, modern technologies not only offer new ways to create music, but can also help people perceive sound in innovative new ways.br

Proceedings of the 6th Conference on Sound and Music Technology | Li Wei | Twarda

pemMachine Learning An Applied Mathematics Introductionem covers the essential mathematics behind all of the following topicsp ul liemKem Nearest Neighbours li liemKem Means Clusteringli liNa ve Bayes Classifierli liRegression Methodsli liSupport Vector Machinesli liSelf-Organizing Mapsli liDecision Treesli liNeural Networksli liReinforcement Learningli ul pThe book includes many real-world examples from a variety of fields includingp ul lifinance volatility modellingli lieconomics interest rates, inflation and GDPli lipolitics classifying politicians according to their voting records, and using speeches to determine whether a politician is left or right wingli libiology recognising flower varieties, and using heights and weights of adults to determine genderli lisociology classifying locations according to crime statisticsli ligambling fruit machines and Blackjackli libusiness classifying the members of his own website to see who will subscribe to his magazine li ul pPaul Wilmott brin

Machine Learning | Wilmott Paul | Twarda

Build the foundational data science skills necessary to work with and better understand complex data science algorithms. This example-driven book provides complete Python coding examples to complement and clarify data science concepts, and enrich the learning experience. Coding examples include visualizations whenever appropriate. The book is a necessary precursor to applying and implementing machine learning algorithms. brThe book is self-contained. All of the math, statistics, stochastic, and programming skills required to master the content are covered. In-depth knowledge of object-oriented programming isn't required because complete examples are provided and explained.briData Science Fundamentals with Python and MongoDBi is an excellent starting point for those interested in pursuing a career in data science. Like any science, the fundamentals of data science are a prerequisite to competency. Without proficiency in mathematics, statistics, data manipulation, and coding, the path to

Data Science Fundamentals for Python and MongoDB | Paper David | Paperback | Twarda

Decision-making in the face of uncertainty is a significant challenge in machine learning, and the multi-armed bandit model is a commonly used framework to address it. This comprehensive and rigorous introduction to the multi-armed bandit problem examines all the major settings, including stochastic, adversarial, and Bayesian frameworks. A focus on both mathematical intuition and carefully worked proofs makes this an excellent reference for established researchers and a helpful resource for graduate students in computer science, engineering, statistics, applied mathematics and economics. Linear bandits receive special attention as one of the most useful models in applications, while other chapters are dedicated to combinatorial bandits, ranking, non-stationary problems, Thompson sampling and pure exploration. The book ends with a peek into the world beyond bandits with an introduction to partial monitoring and learning in Markov decision processes.

Bandit Algorithms | Lattimore Tor | Twarda | Twarda

Random matrices now play a role in many areas of theoretical, applied, and computational mathematics. It is therefore desirable to have tools for studying random matrices that are flexible, easy to use, and powerful. Over the last fifteen years, researchers have developed a remarkable family of results, called matrix concentration inequalities, that achieve all of these goals. This monograph offers an invitation to the field of matrix concentration inequalities. It begins with some history of random matrix theory it describes a flexible model for random matrices that is suitable for many problems and it discusses the most important matrix concentration results. To demonstrate the value of these techniques, the presentation includes examples drawn from statistics, machine learning, optimization, combinatorics, algorithms, scientific computing, and beyond.

An Introduction to Matrix Concentration Inequalities | Tropp Joel a. | Paperback | Twarda

pThe breadth of problems that can be solved with data science is astonishing, and this book provides the required tools and skills to a broad audience. The reader takes a journey into the forms, uses, and abuses of data and models, and learns how to critically examine each step. Python coding and data analysis skills are built from the ground up, with no prior coding experience assumed. The necessary background in computer science, mathematics, and statistics is provided in an approachable manner.ppEach step of the machine learning lifecycle is discussed, from business objective planning to monitoring a model in production. This end-to-end approach supplies the broad view necessary to sidestep many of the pitfalls that can sink a data science project. Detailed examples are provided from a wide range of applications and fields, from fraud detection in banking to breast cancer classification in healthcare. The reader will learn the techniques to accomplish tasks that include predicting o

From Concepts to Code | Tashman Adam P. | Twarda

The discipline that is concerned with the collection, organization, analysis, interpretation, and presentation of data is referred to as statistics. The two main statistical methods that are used in data analysis are descriptive statistics and inferential statistics. Descriptive statistics summarizes data from a sample using indexes such as the mean or standard deviation, whereas inferential statistics draws conclusions from data that are subject to random variation. Statistics is applied in machine learning and data mining, academia, computing, business, mathematics, and arts. Statistical distribution refers to the parameterized mathematical function which provides the probabilities of different outcomes for a random variable. This book is compiled in such a manner, that it will provide in-depth knowledge about the theory and practice of statistical distributions. Also included herein is a detailed explanation of the various concepts and applications of this field. This book aims to e

Advanced Statistics | Morgan Alice | Twarda

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pHow can music withstand the death and destruction brought on by war Global conflicts of the 20th century fundamentally transformed not only national boundaries, power relations, and global economies, but also the arts and culture of every nation involved. An important, unacknowledged aspect of these conflicts is that they have unique musical soundtracks. iMusic in World War IIi explores how music and sound took on radically different dimensions in the United States and Europe before, during, and after World War II. Additionally, the collection examines the impact of radio and film as the disseminators of the war's musical soundtrack. Contributors contend that the European and American soundtrack of World War II was largely one of escapism rather than the lofty, solemn, heroic, and celebratory mode of war music in the past. Furthermore, they explore the variety of experiences of populations forced from their homes and interned in civilian and POW camps in Europe and the United States, 

Music in World War II | Potter Pamela M. | Twarda

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mathematics of machine learning