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Discrete Structures
The aim of this text is to introduce discrete mathematics to beginning students of mathematics or computer science. It does this by bringing some coherency into the seemingly incongruent subjects that compose discrete math, such as logic, set theory, algebra, and combinatorics. It emphasizes their theoretical foundations and illustrates proofs along the way. The book prepares readers for the analysis of algorithms by discussing asymptotic analysis and a discrete calculus for sums. The book also deduces combinatorial methods from the foundations that are laid out. Unlike other texts on this subject, there is a greater emphasis on foundational material that leads to a better understanding. To further assist the reader in grasping and practicing concepts, roughly 690 exercises are provided at various levels of difficulty. Readers are encouraged to study the examples in the text and solve as many of the exercises as possible.The text is intended for freshman or sophomore undergraduate students in mathematics, computer science, or similar majors. The assumed background is precalculus. The chapter dependency chart included is designed to help students, independent readers, and instructors follow a systematic path for learning and teaching the material, with the option to explore material in later chapters.
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Learn Data Analysis with Python
Get started using Python in data analysis with this compact practical guide. This book includes three exercises and a case study on getting data in and out of Python code in the right format. Learn Data Analysis with Python also helps you discover meaning in the data using analysis and shows you how to visualize it. Each lesson is, as much as possible, self-contained to allow you to dip in and out of the examples as your needs dictate. If you are already using Python for data analysis, you will find a number of things that you wish you knew how to do in Python. You can then take these techniques and apply them directly to your own projects.If you aren’t using Python for data analysis, this book takes you through the basics at the beginning to give you a solid foundation in the topic. As you work your way through the book you will have a better of idea of how to use Python for data analysis when you are finished.What You Will LearnGet data into and out of Python codePrepare the data and its formatFind the meaning of the dataVisualize the data using iPythonWho This Book Is For Those who want to learn data analysis using Python. Some experience with Python is recommended but not required, as is some prior experience with data analysis or data science.
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Practical Data Science with Python
Learn to effectively manage data and execute data science projects from start to finish using PythonKey FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook DescriptionPractical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is forThe book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science.The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
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Practical Python Data Wrangling and Data Quality
The world around us is full of data that holds unique insights and valuable stories, and this book will help you uncover them. Whether you already work with data or want to learn more about its possibilities, the examples and techniques in this practical book will help you more easily clean, evaluate, and analyze data so that you can generate meaningful insights and compelling visualizations.Complementing foundational concepts with expert advice, author Susan E. McGregor provides the resources you need to extract, evaluate, and analyze a wide variety of data sources and formats, along with the tools to communicate your findings effectively. This book delivers a methodical, jargon-free way for data practitioners at any level, from true novices to seasoned professionals, to harness the power of data.Use Python 3.8+ to read, write, and transform data from a variety of sourcesUnderstand and use programming basics in Python to wrangle data at scaleOrganize, document, and structure your code using best practicesCollect data from structured data files, web pages, and APIsPerform basic statistical analyses to make meaning from datasetsVisualize and present data in clear and compelling ways
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Fundamental Algorithms for Permutation Groups
This is the first-ever book on computational group theory.It provides extensive and up-to-date coverage of thefundamental algorithms for permutation groups with referenceto aspects of combinatorial group theory, soluble groups,and p-groups where appropriate.The book begins with a constructive introduction to grouptheory and algorithms for computing with small groups,followed by a gradual discussion of the basic ideas of Simsfor computing with very large permutation groups, andconcludes with algorithms that use group homomorphisms, asin the computation of Sylowsubgroups. No background ingroup theory is assumed.The emphasis is on the details of the data structures andimplementation which makes the algorithms effective whenapplied to realistic problems. The algorithms are developedhand-in-hand with the theoretical and practicaljustification.All algorithms are clearly described,examples are given, exercises reinforce understanding, anddetailed bibliographical remarks explain the history andcontext of the work.Much of the later material on homomorphisms, Sylowsubgroups, and soluble permutation groups is new.
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C++ Data Structures: A Laboratory Course
C++ Data Structures: A Laboratory Course exemplifies the active learning experience. With a dynamic learn-by-doing focus, this laboratory manual encourages students to explore data structures by implementing them, a process through which students discover how data structures work and how they can be applied. Providing a framework that offers feedback and support, this text challenges students to exercise their creativity in both programming and analysis. Topics covered include: Text ADT, BlogEntry ADT, Stack ADT, Heap ADT, Weighted Graph ADT, and much more!
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Algorithms and Complexity
This book constitutes the refereed proceedings of the 13th International Conference on Algorithms and Complexity, CIAC 2023, which took place in Larnaca, Cyprus, during June 13–16, 2023. The 25 full papers included in this book were carefully reviewed and selected from 49 submissions. They cover all important areas of research on algorithms and complexity such as algorithm design and analysis; sequential, parallel and distributed algorithms; data structures; computational and structural complexity; lower bounds and limitations of algorithms; randomized and approximation algorithms; parameterized algorithms and parameterized complexity classes; smoothed analysis of algorithms; alternatives to the worst-case analysis of algorithms (e.g., algorithms with predictions), on-line computation and competitive analysis, streaming algorithms, quantum algorithms and complexity, algorithms in algebra, geometry, number theory and combinatorics, computational geometry, algorithmic game theory and mechanism design, algorithmic economics (including auctions and contests), computational learning theory, computational biology and bioinformatics, algorithmic issues in communication networks, algorithms for discrete optimization (including convex optimization) and algorithm engineering.
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Data Structures of Pascal, Algol 68, PL/1 and Ada
This book is intended as a text for a course in programming languages. The pre requisites for such a course are insight in structured programming and knowledge as well as practical experience of at least one (e.g., Pascal) of the programming languages treated in the book. The emphasis is on language concepts rather than on syntactic details. The book covers a number of important language concepts that are related to data struc tures. The comparison of the programming languages Pascal, Algol 68, PL/1 and Ada consists in investigating how these concepts are supported by each of these languages. Interesting evaluation criteria are generality, simplicity, safety, readability and portability. The study of programming languages is based on a simple model called SMALL. This model serves as a didactic vehicle for describing, comparing and evaluating data structures in various programming languages. Each chapter centers around a specific language concept. It consists of a general discussion followed by a number of language sections, one for each of the languages Pascal, Algol 68, PL/1 and Ada. Each of these sections contains a number of illustrating program fragments written in the programming language concerned. For each program fragment in one language, there is an analogous fragment in the others. The book can be read "vertically" so that the programming languages Pascal, Algol 68, PL/1 and Ada are encountered in that order several times. A "horizontal" reading of the book would consist in selecting only those sections which only concern one language.
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Data Structures for Computational Statistics
Since the beginning of the seventies computer hardware is available to use programmable computers for various tasks. During the nineties the hardware has developed from the big main frames to personal workstations. Nowadays it is not only the hardware which is much more powerful, but workstations can do much more work than a main frame, compared to the seventies. In parallel we find a specialization in the software. Languages like COBOL for business orientated programming or Fortran for scientific computing only marked the beginning. The introduction of personal computers in the eighties gave new impulses for even further development, already at the beginning of the seven ties some special languages like SAS or SPSS were available for statisticians. Now that personal computers have become very popular the number of pro grams start to explode. Today we will find a wide variety of programs for almost any statistical purpose (Koch & Haag 1995).
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Combinatorial Algorithms
This book constitutes the thoroughly refereedpost-workshop proceedings for the 26 International Workshop on combinatorialAlgorithms, IWOCA 2015, held in Verona, Italy, in October 2015.The 29 revised full papers presented were carefullyreviewed and selected from a total of 90 submissions. The topics of the papersinclude algorithms and data structures (including sequential, parallel,distributed, approximation, probabilistic, randomised, and on-line algorithms),algorithms on strings and graphs; applications (bioinformatics, music analysis,networking, and others); combinatorics on words; combinatorial enumeration;combinatorial optimization; complexity theory; computational biology;compression and information retrieval; cryptography and information security;decompositions and combinatorial designs; discrete and computational geometry;graph drawing and labeling; graph theory.
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Behavioral Data Analysis with R and Python
Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis.Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, this practical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately.Understand the specifics of behavioral dataExplore the differences between measurement and predictionLearn how to clean and prepare behavioral dataDesign and analyze experiments to drive optimal business decisionsUse behavioral data to understand and measure cause and effectSegment customers in a transparent and insightful way
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Algebra and Geometry with Python
This book teaches algebra and geometry. The authors dedicate chapters to the key issues of matrices, linear equations, matrix algorithms, vector spaces, lines, planes, second-order curves, and elliptic curves. The text is supported throughout with problems, and the authors have included source code in Python in the book. The book is suitable for advanced undergraduate and graduate students in computer science.
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Practical Computer Architecture with Python and ARM
Learn computer architecture with Python and ARM, simulating assembly program execution and designing a computer simulatorPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesBuild a computer simulator with Python: Learn computer architecture by designing and constructing a simulatorPython for architecture: Use Python to simulate and execute assembly language instructionsARM programming on Raspberry Pi: Explore ARM assembly language and run programs on Raspberry PiBook DescriptionThis comprehensive guide offers a unique and immersive learning experience by combining Python programming with ARM architecture.Starting with an introduction to computer architecture and the flow of data within a computer system, you’ll progress to building your own interpreter using Python. You’ll see how this foundation enables the simulation of computer operations and learn ways to enhance a simulator by adding new instructions and displaying improved results.As you advance, you’ll explore the TC1 Assembler and Simulator Program to gain insights into instruction analysis and explore practical examples of simulators. This will help you build essential skills in understanding complex computer instructions, strengthening your grasp of computer architecture. Moreover, you’ll be introduced to the Raspberry Pi operating system, preparing you to delve into the detailed language of the ARM computer. This includes exploring the ARM instruction set architecture, data-processing instructions, subroutines, and the stack.With clear explanations, practical examples, and coding exercises, this resource will enable you to design and construct your own computer simulator, simulate assembly language programs, and leverage the Raspberry Pi for ARM programming.What you will learnMaster the core principles of computer architectureUnderstand the role of registers, memory, and data flow in computersDiscover how to design and implement a computer simulator using PythonSimulate and execute assembly language programs on the simulatorEnhance the simulator using new instructions for improved outputAnalyze complex computer instructions for deeper architectural understandingExplore the ARM instruction set and data processing on the Raspberry PiDevelop proficiency in writing, assembling, and running ARM code on the Raspberry PiWho this book is forThis book is for university students studying computer science, particularly those enrolled in a computer architecture module. With its practical approach and succinct explanations, it is also suitable for hobbyists, enthusiasts, and self-learners seeking a deeper understanding of computer systems. The book assumes foundational knowledge of number bases, binary arithmetic, and Boolean logic concepts. While it primarily caters to the computer science field, this book is less geared toward electrical or electronics engineering.
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Algorithms and Complexity
This book constitutes the refereed proceedings of the Third Italian Conference on Algorithms and Complexity, CIAC'97, held in Rome, Italy in March 1997.The 25 revised full papers included in the volume were carefully selected from a total of 74 submissions; also included is an invited paper and an invited abstract. All in all, the papers present an interesting snapshot of current research activities and recent results in theory and applications of sequential, distributed, and parallel algorithms, data structures, and computational complexity.
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Python and R for the Modern Data Scientist
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. With this book, data scientists from the Python and R communities will learn how to speak the dialects of each language. By recognizing the strengths of working with both, you'll discover new ways to accomplish data science tasks and expand your skill set.Authors Boyan Angelov and Rick Scavetta explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. Not only will you learn how to use Python and R together in real-world settings, but you'll also broaden your knowledge and job opportunities by working as a bilingual data scientist.Learn Python and R from the perspective of your current languageUnderstand the strengths and weaknesses of each languageIdentify use cases where one language is better suited than the otherUnderstand the modern open source ecosystem available for both, including packages, frameworks, and workflowsLearn how to integrate R and Python in a single workflowFollow a real-world case study that demonstrates ways to use these languages together
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Scientific Computing with Python
Leverage this example-packed, comprehensive guide for all your Python computational needsKey FeaturesLearn the first steps within Python to highly specialized conceptsExplore examples and code snippets taken from typical programming situations within scientific computing.Delve into essential computer science concepts like iterating, object-oriented programming, testing, and MPI presented in strong connection to applications within scientific computing.Book DescriptionPython has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python.This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations.By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.What you will learnUnderstand the building blocks of computational mathematics, linear algebra, and related Python objectsUse Matplotlib to create high-quality figures and graphics to draw and visualize resultsApply object-oriented programming (OOP) to scientific computing in PythonDiscover how to use pandas to enter the world of data processingHandle exceptions for writing reliable and usable codeCover manual and automatic aspects of testing for scientific programmingGet to grips with parallel computing to increase computation speedWho this book is forThis book is for students with a mathematical background, university teachers designing modern courses in programming, data scientists, researchers, developers, and anyone who wants to perform scientific computation in Python.
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Adaptive Machine Learning Algorithms with Python
Learn to use adaptive algorithms to solve real-world streaming data problems. This book covers a multitude of data processing challenges, ranging from the simple to the complex. At each step, you will gain insight into real-world use cases, find solutions, explore code used to solve these problems, and create new algorithms for your own use.Authors Chanchal Chatterjee and Vwani P. Roychowdhury begin by introducing a common framework for creating adaptive algorithms, and demonstrating how to use it to address various streaming data issues. Examples range from using matrix functions to solve machine learning and data analysis problems to more critical edge computation problems. They handle time-varying, non-stationary data with minimal compute, memory, latency, and bandwidth. Upon finishing this book, you will have a solid understanding of how to solve adaptive machine learning and data analytics problems and be able to derive new algorithms for your own use cases. You will also come away with solutions to high volume time-varying data with high dimensionality in a low compute, low latency environment.What You Will LearnApply adaptive algorithms to practical applications and examplesUnderstand the relevant data representation features and computational models for time-varying multi-dimensional dataDerive adaptive algorithms for mean, median, covariance, eigenvectors (PCA) and generalized eigenvectors with experiments on real dataSpeed up your algorithms and put them to use on real-world stationary and non-stationary dataMaster the applications of adaptive algorithms on critical edge device computation applicationsWho This Book Is ForMachine learning engineers, data scientist and architects, software engineers and architects handling edge device computation and data management.
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Generative AI Apps with LangChain and Python
Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain’s components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you’ll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries.Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you’ll learn-by-be doing, enhancing your career possibilities in today’s rapidly evolving landscape.What You Will Learn Understand different types of LLMs and how to select the right ones for responsible AI.Structure effective prompts.Master LangChain concepts, such as chains, models, memory, and agents.Apply embeddings effectively for search, content comparison, and understanding similarity.Setup and integrate Pinecone vector database for indexing, structuring data, and search.Build Q & A applications for multiple doc formats.Develop multi-step AI workflow apps using LangChain agents.Who This Book Is ForPython programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.
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Applied Unsupervised Learning with Python
Design clever algorithms that can uncover interesting structures and hidden relationships in unstructured, unlabeled dataKey FeaturesLearn how to select the most suitable Python library to solve your problemCompare k-Nearest Neighbor (k-NN) and non-parametric methods and decide when to use themDelve into the applications of neural networks using real-world datasetsBook DescriptionUnsupervised learning is a useful and practical solution in situations where labeled data is not available. Applied Unsupervised Learning with Python guides you on the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The course begins by explaining how basic clustering works to find similar data points in a set. Once you are well versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. You will complete the course by challenging yourself through various interesting activities such as performing a Market Basket Analysis and identifying relationships between different merchandises. By the end of this course, you will have the skills you need to confidently build your own models using Python.What you will learnUnderstand the basics and importance of clusteringBuild k-means, hierarchical, and DBSCAN clustering algorithms from scratch with built-in packagesExplore dimensionality reduction and its applicationsUse scikit-learn (sklearn) to implement and analyse principal component analysis (PCA)on the Iris datasetEmploy Keras to build autoencoder models for the CIFAR-10 datasetApply the Apriori algorithm with machine learning extensions (Mlxtend) to study transaction dataWho this book is forThis course is designed for developers, data scientists, and machine learning enthusiasts who are interested in unsupervised learning. Some familiarity with Python programming along with basic knowledge of mathematical concepts including exponents, square roots, means, and medians will be beneficial.
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Large-Scale Data Analytics with Python and Spark
Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments.
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Genetic Programming and Data Structures
Computers that `program themselves' has long been an aim of computer scientists. Recently genetic programming (GP) has started to show its promise by automatically evolving programs. Indeed in a small number of problems GP has evolved programs whose performance is similar to or even slightly better than that of programs written by people. The main thrust of GP has been to automatically create functions. While these can be of great use they contain no memory and relatively little work has addressed automatic creation of program code including stored data. This issue is the main focus of Genetic Programming, and Data Structures: Genetic Programming + Data Structures = Automatic Programming!. This book is motivated by the observation from software engineering that data abstraction (e.g., via abstract data types) is essential in programs created by human programmers. This book shows that abstract data types can be similarly beneficial to the automatic production of programs using GP. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! shows how abstract data types (stacks, queues and lists) can be evolved using genetic programming, demonstrates how GP can evolve general programs which solve the nested brackets problem, recognises a Dyck context free language, and implements a simple four function calculator. In these cases, an appropriate data structure is beneficial compared to simple indexed memory. This book also includes a survey of GP, with a critical review of experiments with evolving memory, and reports investigations of real world electrical network maintenance scheduling problems that demonstrate that Genetic Algorithms can find low cost viable solutions to such problems. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! should be of direct interest to computer scientists doing research on genetic programming, genetic algorithms, data structures, and artificial intelligence. In addition, this book will be of interest to practitioners working in all of these areas and to those interested in automatic programming.
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Hands-On Genetic Algorithms with Python
Explore the ever-growing world of genetic algorithms to build and enhance AI applications involving search, optimization, machine learning, deep learning, NLP, and XAI using Python librariesKey FeaturesLearn how to implement genetic algorithms using Python libraries DEAP, scikit-learn, and NumPyTake advantage of cloud computing technology to increase the performance of your solutionsDiscover bio-inspired algorithms such as particle swarm optimization (PSO) and NEATPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWritten by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms.After an introduction to genetic algorithms and their principles of operation, you’ll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you’ll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You’ll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications.By the end of this book, you’ll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.What you will learnUse genetic algorithms to solve planning, scheduling, gaming, and analytics problemsCreate reinforcement learning, NLP, and explainable AI applicationsEnhance the performance of ML models and optimize deep learning architectureDeploy genetic algorithms using client-server architectures, enhancing scalability and computational efficiencyExplore how images can be reconstructed using a set of semi-transparent shapesDelve into topics like elitism, niching, and multiplicity in genetic solutions to enhance optimization strategies and solution diversityWho this book is forIf you’re a data scientist, software developer, AI enthusiast who wants to break into the world of genetic algorithms and apply them to real-world, intelligent applications as quickly as possible, this book is for you. Working knowledge of the Python programming language is required to get started with this book.
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Algorithms and Complexity
The papers in this volume were presented at the Fourth Italian Conference on Algorithms and Complexity (CIAC 2000). The conference took place on March 1-3, 2000, in Rome (Italy), at the conference center of the University of Rome \La Sapienza". This conference was born in 1990 as a national meeting to be held every three years for Italian researchers in algorithms, data structures, complexity, and parallel and distributed computing. Due to a signi cant participation of foreign reaserchers, starting from the second conference, CIAC evolved into an international conference. In response to the call for papers for CIAC 2000, there were 41 subm- sions, from which the program committee selected 21 papers for presentation at the conference. Each paper was evaluated by at least three program committee members. In addition to the selected papers, the organizing committee invited Giorgio Ausiello, Narsingh Deo, Walter Ruzzo, and Shmuel Zaks to give plenary lectures at the conference. We wish to express our appreciation to all the authors of the submitted papers, to the program committee members and the referees, to the organizing committee, and to the plenary lecturers who accepted our invitation.
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Introduction to Computational Models with Python
Introduction to Computational Models with Python explains how to implement computational models using the flexible and easy-to-use Python programming language. The book uses the Python programming language interpreter and several packages from the huge Python Library that improve the performance of numerical computing, such as the Numpy and Scipy modules. The Python source code and data files are available on the author’s website.The book’s five sections present: An overview of problem solving and simple Python programs, introducing the basic models and techniques for designing and implementing problem solutions, independent of software and hardware toolsProgramming principles with the Python programming language, covering basic programming concepts, data definitions, programming structures with flowcharts and pseudo-code, solving problems, and algorithmsPython lists, arrays, basic data structures, object orientation, linked lists, recursion, and running programs under LinuxImplementation of computational models with Python using Numpy, with examples and case studies The modeling of linear optimization problems, from problem formulation to implementation of computational modelsThis book introduces the principles of computational modeling as well as the approaches of multi- and interdisciplinary computing to beginners in the field. It provides the foundation for more advanced studies in scientific computing, including parallel computing using MPI, grid computing, and other methods and techniques used in high-performance computing.
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Hands-On Unsupervised Learning with Python
Discover the skill-sets required to implement various approaches to Machine Learning with PythonKey FeaturesExplore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and moreBuild your own neural network models using modern Python librariesPractical examples show you how to implement different machine learning and deep learning techniquesBook DescriptionUnsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python.This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images.By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges.What you will learnUse cluster algorithms to identify and optimize natural groups of dataExplore advanced non-linear and hierarchical clustering in actionSoft label assignments for fuzzy c-means and Gaussian mixture modelsDetect anomalies through density estimationPerform principal component analysis using neural network modelsCreate unsupervised models using GANsWho this book is forThis book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.
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Introduction to R and Python for Data Analysis
An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more, save time, whilst reinforcing the shared concepts and differences of the systems. This tandem learning is highly useful for students, helping them to become literate in both languages, and develop skills which will be handy after their studies. This book presumes no prior experience with computing, and is intended to be used by students from a variety of backgrounds. The side-by-side formatting of this book helps introductory graduate students quickly grasp the basics of R and Python, with the exercises providing helping them to teach themselves the skills they will need upon the completion of their course, as employers now ask for competency in both R and Python. Teachers and lecturers will also find this book useful in their teaching, providing a singular work to help ensure their students are well trained in both computer languages. All data for exercises can be found here: https://github.com/tbrown122387/randpythonbook/tree/master/data. Instructors can access the solutions manual via the book's website.Key features: - Teaches R and Python in a "side-by-side" way. - Examples are tailored to aspiring data scientists and statisticians, not software engineers. - Designed for introductory graduate students.- Does not assume any mathematical background.
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Algorithms and Computation
This book constitutes the refereed proceedings of the 25th International Symposium on Algorithms and Computation, ISAAC 2014, held in Jeonju, Korea, in December 2014. The 60 revised full papers presented together with 2 invited talks were carefully reviewed and selected from 171 submissions for inclusion in the book. The focus of the volume in on the following topics: computational geometry, combinatorial optimization, graph algorithms: enumeration, matching and assignment, data structures and algorithms, fixed-parameter tractable algorithms, scheduling algorithms, computational complexity, computational complexity, approximation algorithms, graph theory and algorithms, online and approximation algorithms, and network and scheduling algorithms.
R 4,354.20
R 641.00
Algorithms and Complexity
The papers in this volume were presented at the 5th Italian Conference on AlgorithmsandComplexity(CIAC2003). Theconferencetookplaceduring May 28-30, 2003, in Rome, Italy, at the Conference Centre of the University of Rome "La Sapienza. " CIAC started in 1990 as a national meeting to be held every three years for Italian researchers in algorithms, data structures, complexity theory, and parallelanddistributedcomputing. Duetoasigni?cantparticipationofforeign researchers, starting from the second edition, CIAC evolved into an international conference. However,alltheeditionsofCIAChavebeenheldinRome. The proceedings of CIAC were published by World Scienti?c for the ?rst edition and by Springer-Verlag in the Lecture Notes in Computer Science series (volumes 778,1203and1767)forthesubsequenteditions. Aselectionofthepapersofthe fourth edition was published in a special issue of Theoretical Computer Science Vol. 285(1),2002. Thisyearweexpecttopublishanextendedversionofselected papers presented at the conference in a special issue of the journal Theory of Computing Systems.In response to the call for papers for CIAC 2003, 57 papers were subm- ted, from which the Program Committee selected 23 papers for presentation at theconferencefrom18countries. Eachpaperwasevaluatedbyatleastthree Program Committee members with the help of 63 external reviewers. In addition to the selected papers, the Organizing Committee invited CharlesE. Leiserson(Cambridge),DavidPeleg(Rehovot),MichaelO. Rabin (CambridgeandJerusalem),JohnE. Savage(Providence),andLucaTrevisan (Berkeley)togiveplenarylecturesattheconference. Moreover,threetutorials byDavidPeleg,JaymeL. Szwarc?ter(RiodeJaneiro)andLucaTrevisanwere o?ered in the days preceding the conference. We wish to express our appreciation to all the authors of the submitted - pers, to the Program Committee members and the referees, to the Organizing Committee, and to the plenary and tutorial lecturers who accepted our in- tation.
R 2,015.00
Applied Algorithms
This book constitutes the refereed proceedings of the Third International Conference on Applied Algorithms, ICAA 2026, held in Kolkata, India, during January 7–9, 2026.The 33 full papers presented in this book were carefully reviewed and selected from 92 submissions. They focus on the design, analysis, implementation, and experimental evaluation of efficient algorithms and data structures aimed at solving practical, real-world problems.
R 3,153.71
Learn to Program with Python 3
Move from zero knowledge of programming to comfortably writing small to medium-sized programs in Python. Fully updated for Python 3, with code and examples throughout, the book explains Python coding with an accessible, step-by-step approach designed to bring you comfortably into the world of software development.Real–world analogies make the material understandable, with a wide variety of well-documented examples to illustrate each concept. Along the way, you’ll develop short programs through a series of coding challenges that reinforce the content of the chapters.Learn to Program with Python 3 guides you with material developed in the author's university computer science courses. The author's conversational style feels like you're working with a personal tutor. All material is thoughtfully laid out, each lesson building on previous ones. What You'll Learn Understand programming basics with Python, based on material developed in the author's college coursesLearn core concepts: variables, functions, conditionals, loops, lists, strings, and moreExplore example programs including simple games you can program and customizeBuild modules to reuse your own codeWho This Book Is ForThis book assumes no prior programming experience, and would be appropriate as text for a high school or college introduction to computer science.
R 1,326.49
R 1,527.00
Functional Data Structures in R
Get an introduction to functional data structures using R and write more effective code and gain performance for your programs. This book teaches you workarounds because data in functional languages is not mutable: for example you’ll learn how to change variable-value bindings by modifying environments, which can be exploited to emulate pointers and implement traditional data structures. You’ll also see how, by abandoning traditional data structures, you can manipulate structures by building new versions rather than modifying them. You’ll discover how these so-called functional data structures are different from the traditional data structures you might know, but are worth understanding to do serious algorithmic programming in a functional language such as R.By the end of Functional Data Structures in R, you’ll understand the choices to make in order to most effectively work with data structures when you cannot modify the data itself. These techniques are especially applicable for algorithmic development important in big data, finance, and other data science applications.What You'll LearnCarry out algorithmic programming in R Use abstract data structures Work with both immutable and persistent data Emulate pointers and implement traditional data structures in RBuild new versions of traditional data structures that are knownWho This Book Is ForExperienced or advanced programmers with at least a comfort level with R. Some experience with data structures recommended.
R 878.03
