Search results for "Building LLM Applications with Python"
Items we need to order for you
UML 2 Semantics and Applications
With an up-to-date view of the results of UML semantics and the practical applications of semantics development, this book presents the only coherent and integrated account of the leading UML 2 semantics work and its applications. With contributions from renowned experts in the area of UML 2 semantics and its applications, the book covers semantics of UML 2 diagrams, OCL, state machine diagrams, interactions, use cases, and activities. UML Semantics and Applications is an excellent reference for researchers and students in the model-driven development community.
R 5,111.00
Getting Started with Python
Harness the power of Python objects and data structures to implement algorithms for analyzing your data and efficiently extracting informationKey FeaturesTurn your designs into working software by learning the Python syntaxWrite robust code with a solid understanding of Python data structuresUnderstand when to use the functional or the OOP approachBook DescriptionThis Learning Path helps you get comfortable with the world of Python. It starts with a thorough and practical introduction to Python. You’ll quickly start writing programs, building websites, and working with data by harnessing Python's renowned data science libraries. With the power of linked lists, binary searches, and sorting algorithms, you'll easily create complex data structures, such as graphs, stacks, and queues. After understanding cooperative inheritance, you'll expertly raise, handle, and manipulate exceptions. You will effortlessly integrate the object-oriented and not-so-object-oriented aspects of Python, and create maintainable applications using higher level design patterns. Once you’ve covered core topics, you’ll understand the joy of unit testing and just how easy it is to create unit tests.By the end of this Learning Path, you will have built components that are easy to understand, debug, and can be used across different applications.This Learning Path includes content from the following Packt products:Learn Python Programming - Second Edition by Fabrizio RomanoPython Data Structures and Algorithms by Benjamin BakaPython 3 Object-Oriented Programming by Dusty PhillipsWhat you will learnUse data structures and control flow to write codeUse functions to bundle together a sequence of instructionsImplement objects in Python by creating classes and defining methodsDesign public interfaces using abstraction, encapsulation and information hidingRaise, define, and manipulate exceptions using special error objectsCreate bulletproof and reliable software by writing unit testsLearn the common programming patterns and algorithms used in PythonWho this book is forIf you are relatively new to coding and want to write scripts or programs to accomplish tasks using Python, or if you are an object-oriented programmer for other languages and seeking a leg up in the world of Python, then this Learning Path is for you. Though not essential, it will help you to have basic knowledge of programming and OOP.
R 2,297.52
Hands-On Deep Learning Algorithms with Python
Understand basic to advanced deep learning algorithms, the mathematical principles behind them, and their practical applications.Key FeaturesGet up-to-speed with building your own neural networks from scratch Gain insights into the mathematical principles behind deep learning algorithmsImplement popular deep learning algorithms such as CNNs, RNNs, and more using TensorFlowBook DescriptionDeep learning is one of the most popular domains in the AI space, allowing you to develop multi-layered models of varying complexities. This book introduces you to popular deep learning algorithms—from basic to advanced—and shows you how to implement them from scratch using TensorFlow. Throughout the book, you will gain insights into each algorithm, the mathematical principles behind it, and how to implement it in the best possible manner. The book starts by explaining how you can build your own neural networks, followed by introducing you to TensorFlow, the powerful Python-based library for machine learning and deep learning. Moving on, you will get up to speed with gradient descent variants, such as NAG, AMSGrad, AdaDelta, Adam, and Nadam. The book will then provide you with insights into RNNs and LSTM and how to generate song lyrics with RNN. Next, you will master the math for convolutional and capsule networks, widely used for image recognition tasks. Then you learn how machines understand the semantics of words and documents using CBOW, skip-gram, and PV-DM. Afterward, you will explore various GANs, including InfoGAN and LSGAN, and autoencoders, such as contractive autoencoders and VAE. By the end of this book, you will be equipped with all the skills you need to implement deep learning in your own projects.What you will learnImplement basic-to-advanced deep learning algorithmsMaster the mathematics behind deep learning algorithmsBecome familiar with gradient descent and its variants, such as AMSGrad, AdaDelta, Adam, and NadamImplement recurrent networks, such as RNN, LSTM, GRU, and seq2seq modelsUnderstand how machines interpret images using CNN and capsule networksImplement different types of generative adversarial network, such as CGAN, CycleGAN, and StackGANExplore various types of autoencoder, such as Sparse autoencoders, DAE, CAE, and VAEWho this book is forIf you are a machine learning engineer, data scientist, AI developer, or simply want to focus on neural networks and deep learning, this book is for you. Those who are completely new to deep learning, but have some experience in machine learning and Python programming, will also find the book very helpful.
R 1,335.00
Learning Geospatial Analysis with Python
Learn the core concepts of geospatial data analysis for building actionable and insightful GIS applicationsKey FeaturesCreate GIS solutions using the new features introduced in Python 3.7Explore a range of GIS tools and libraries such as PostGIS, QGIS, and PROJLearn to automate geospatial analysis workflows using Python and JupyterBook DescriptionGeospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python.This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data.By the end of the book, you'll be able to build a generic corporate system, which can be implemented in any organization to manage customer support requests and field support personnel.What you will learnAutomate geospatial analysis workflows using PythonCode the simplest possible GIS in just 60 lines of PythonCreate thematic maps with Python tools such as PyShp, OGR, and the Python Imaging LibraryUnderstand the different formats that geospatial data comes inProduce elevation contours using Python toolsCreate flood inundation modelsApply geospatial analysis to real-time data tracking and storm chasingWho this book is forThis book is for Python developers, researchers, or analysts who want to perform geospatial modeling and GIS analysis with Python. Basic knowledge of digital mapping and analysis using Python or other scripting languages will be helpful.
R 2,052.00
Bayesian Analysis with Python - Third Edition
Learn the fundamentals of Bayesian modeling using state-of-the-art Python libraries, such as PyMC, ArviZ, Bambi, and more, guided by an experienced Bayesian modeler who contributes to these librariesKey Features: Conduct Bayesian data analysis with step-by-step guidanceGain insight into a modern, practical, and computational approach to Bayesian statistical modelingEnhance your learning with best practices through sample problems and practice exercisesPurchase of the print or Kindle book includes a free PDF eBook.Book Description: The third edition of Bayesian Analysis with Python serves as an introduction to the main concepts of applied Bayesian modeling using PyMC, a state-of-the-art probabilistic programming library, and other libraries that support and facilitate modeling like ArviZ, for exploratory analysis of Bayesian models; Bambi, for flexible and easy hierarchical linear modeling; PreliZ, for prior elicitation; PyMC-BART, for flexible non-parametric regression; and Kulprit, for variable selection.In this updated edition, a brief and conceptual introduction to probability theory enhances your learning journey by introducing new topics like Bayesian additive regression trees (BART), featuring updated examples. Refined explanations, informed by feedback and experience from previous editions, underscore the book's emphasis on Bayesian statistics. You will explore various models, including hierarchical models, generalized linear models for regression and classification, mixture models, Gaussian processes, and BART, using synthetic and real datasets.By the end of this book, you will possess a functional understanding of probabilistic modeling, enabling you to design and implement Bayesian models for your data science challenges. You'll be well-prepared to delve into more advanced material or specialized statistical modeling if the need arises.What You Will Learn: Build probabilistic models using PyMC and BambiAnalyze and interpret probabilistic models with ArviZAcquire the skills to sanity-check models and modify them if necessaryBuild better models with prior and posterior predictive checksLearn the advantages and caveats of hierarchical modelsCompare models and choose between alternative onesInterpret results and apply your knowledge to real-world problemsExplore common models from a unified probabilistic perspectiveApply the Bayesian framework's flexibility for probabilistic thinkingWho this book is for: If you are a student, data scientist, researcher, or developer looking to get started with Bayesian data analysis and probabilistic programming, this book is for you. The book is introductory, so no previous statistical knowledge is required, although some experience in using Python and scientific libraries like NumPy is expected.Table of Contents
R 2,377.00
Linear Algebra with Python
This textbook is for those who want to learn linear algebra from the basics. After a brief mathematical introduction, it provides the standard curriculum of linear algebra based on an abstract linear space. It covers, among other aspects: linear mappings and their matrix representations, basis, and dimension; matrix invariants, inner products, and norms; eigenvalues and eigenvectors; and Jordan normal forms. Detailed and self-contained proofs as well as descriptions are given for all theorems, formulas, and algorithms.A unified overview of linear structures is presented by developing linear algebra from the perspective of functional analysis. Advanced topics such as function space are taken up, along with Fourier analysis, the Perron–Frobenius theorem, linear differential equations, the state transition matrix and the generalized inverse matrix, singular value decomposition, tensor products, and linear regression models. These all provide a bridge to more specialized theories based on linear algebra in mathematics, physics, engineering, economics, and social sciences.Python is used throughout the book to explain linear algebra. Learning with Python interactively, readers will naturally become accustomed to Python coding. By using Python’s libraries NumPy, Matplotlib, VPython, and SymPy, readers can easily perform large-scale matrix calculations, visualization of calculation results, and symbolic computations. All the codes in this book can be executed on both Windows and macOS and also on Raspberry Pi.
R 2,381.00
Calculus With Applications
Burstein, and Lax's Calculus with Applications and Computing offers meaningful explanations of the important theorems of single variable calculus. Written with students in mathematics, the physical sciences, and engineering in mind, and revised with their help, it shows that the themes of calculation, approximation, and modeling are central to mathematics and the main ideas of single variable calculus. This edition brings the innovation of the first edition to a new generation of students. New sections in this book use simple, elementary examples to show that when applying calculus concepts to approximations of functions, uniform convergence is more natural and easier to use than point-wise convergence. As in the original, this edition includes material that is essential for students in science and engineering, including an elementary introduction to complex numbers and complex-valued functions, applications of calculus to modeling vibrations and population dynamics, and an introduction to probability and information theory.
R 2,399.25
Numerical Methods in Physics with Python
Bringing together idiomatic Python programming, foundational numerical methods, and physics applications, this is an ideal standalone textbook for courses on computational physics. All the frequently used numerical methods in physics are explained, including foundational techniques and hidden gems on topics such as linear algebra, differential equations, root-finding, interpolation, and integration. The second edition of this introductory book features several new codes and 140 new problems (many on physics applications), as well as new sections on the singular-value decomposition, derivative-free optimization, Bayesian linear regression, neural networks, and partial differential equations. The last section in each chapter is an in-depth project, tackling physics problems that cannot be solved without the use of a computer. Written primarily for students studying computational physics, this textbook brings the non-specialist quickly up to speed with Python before looking in detail at the numerical methods often used in the subject.
R 5,259.00
Sparse Estimation with Math and Python
The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building Python programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers’ insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter.This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis.This book is one of a series of textbooks in machine learning by the same Author. Other titles are: Statistical Learning with Math and R (https://www.springer.com/gp/book/9789811575679)Statistical Learning with Math and Pyth (https://www.springer.com/gp/book/9789811578762)Sparse Estimation with Math and R
R 1,649.00
R 275.00
R 1,343.00
Data Wrangling with Python
Simplify your ETL processes with these hands-on data hygiene tips, tricks, and best practices.Key FeaturesFocus on the basics of data wranglingStudy various ways to extract the most out of your data in less timeBoost your learning curve with bonus topics like random data generation and data integrity checksBook DescriptionFor data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You’ll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you’ll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets. By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently. What you will learnUse and manipulate complex and simple data structuresHarness the full potential of DataFrames and numpy.array at run timePerform web scraping with BeautifulSoup4 and html5libExecute advanced string search and manipulation with RegEXHandle outliers and perform data imputation with PandasUse descriptive statistics and plotting techniquesPractice data wrangling and modeling using data generation techniquesWho this book is forData Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have rudimentary knowledge of relational database and SQL.
R 1,498.00
Building Energy Modeling with OpenStudio
This textbook teaches the fundamentals of building energy modeling and analysis using open source example applications built with the US DOE’s OpenStudio modeling platform and EnergyPlus simulation engine. Designed by researchers at US National Laboratories to support a new generation of high performance buildings, EnergyPlus and OpenStudio are revolutionizing how building energy modeling is taught in universities and applied by professional architects and engineers around the world. The authors, all researchers at National Renewable Energy Laboratory and members of the OpenStudio software development team, present modeling concepts using open source software that may be generally applied using a variety of software tools commonly used by design professionals. The book also discusses modeling process automation in the context of OpenStudio Measures—small self-contained scripts that can transform energy models and their data—to save time and effort. They illustrate key concepts through a sophisticated example problem that evolves in complexity throughout the book. The text also examines advanced topics including daylighting, parametric analysis, uncertainty analysis, design optimization, and model calibration. Building Energy Modeling with OpenStudio teaches students to become sophisticated modelers rather than simply proficient software users. It supports undergraduate and graduate building energy courses in Architecture, and in Mechanical, Civil, Architectural, and Sustainability Engineering.
R 3,573.32
R 641.00
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.
R 1,889.00
Building Feature Extraction with Machine Learning
Big geospatial datasets created by large infrastructure projects require massive computing resources to process. Feature extraction is a process used to reduce the initial set of raw data for manageable image processing, and machine learning (ML) is the science that supports it. This book focuses on feature extraction methods for optical geospatial data using ML. It is a practical guide for professionals and graduate students who are starting a career in information extraction. It explains spatial feature extraction in an easy-to-understand way and includes real case studies on how to collect height values for spatial features, how to develop 3D models in a map context, and others.FeaturesProvides the basics of feature extraction methods and applications along with the fundamentals of machine learningDiscusses in detail the application of machine learning techniques in geospatial building feature extractionExplains the methods for estimating object height from optical satellite remote sensing images using PythonIncludes case studies that demonstrate the use of machine learning models for building footprint extraction and photogrammetric methods for height assessmentHighlights the potential of machine learning and geospatial technology for future project developmentsThis book will be of interest to professionals, researchers, and graduate students in geoscience and earth observation, machine learning and data science, civil engineers, and urban planners.
R 5,929.30
R 1,310.00
R 733.00
Mastering Financial Markets with Python
Master Technical Analysis in the Age of Python and Innovation Financial markets are evolving, and so should your strategies. Mastering Financial Markets with Python: New Horizons in Technical Analysis bridges the gap between traditional methods and the new era of data-driven analysis. This book equips you with the skills to design, implement, and evaluate advanced technical analysis techniques all through a practical, code-first approach. Inside, you will learn how to: Rethink and modernize classic indicators with Python. Build advanced volatility and moving average indicators. Detect and analyze harmonic and reversal patterns automatically. Detect and analyze timing patterns automatically. Apply enhanced Fibonacci techniques and alternative charting systems. Develop a new generation of technical indicators tailored to modern markets. This book provides hands-on tools to master today's fast-moving financial landscape. By combining innovation with Python, you'll gain a deeper understanding of markets and build smarter, more adaptive strategies. Why buy it now? Because trading edges are fleeting, and those who master Python-driven technical analysis will be the ones shaping the markets of tomorrow.
R 954.84
R 565.35
Advances in Conceptual Modeling - Foundations and Applications
This book constitutes the refereed joint proceedings of six workshops held in conjunction with the 26th International Conference on Conceptual Modeling. Topics include conceptual modeling for life sciences applications, foundations and practices of UML, ontologies and information systems for the semantic Web , quality of information systems, requirements, intentions and goals in conceptual modeling, and semantic and conceptual issues in geographic information systems.
R 1,579.74
UML Modeling Languages and Applications
The UML 2004 conference was held in Lisbon (Portugal) from October 11 through October 15, 2004. It was the seventh conference in a series of annual events that started in 1998. UML has rapidly become one of the leading venues to present and discuss the development of object-oriented modeling. In order to re?ect the changes in the ?eld, the UML conference series will be continued from 2005 onwards under the name MODELS (Model Driven En- neering, Languages and Systems). Inane?orttomakethisyear’sconferencemoreusefulande?ectiveforawider community, including academics and practitioners working in areas related to UML and modeling in general, a set of satellite events was organized, including workshopsdedicatedtospeci?cresearchtopics,anindustrytrack,aposter/demo session, and a tools exhibit. This volume is a compilation of the contributions presented at these satellite events. Workshops at UML 2004 took place during the ?rst three days of the conference(fromOctober10to12). Followingthetraditionofprevious UML conferences, UML 2004workshopsprovidedacollaborativeforumforgroups of (typically 15 to 30) participants to exchange recent or preliminary results, to conduct intensive discussions on a particular topic, or to coordinate e?orts between representatives of a technical community. Ten workshops were held, covering a variety of hot topics, which have been covered in the workshop - ports contained in this volume. Each workshop lasted for a full day. A novelty with respect to previous UML conferences was the inclusion of a Doctoral Symposium, which was well received, to provide an explicit space for young - searchers developing their thesis on some aspect related to UML.
R 1,558.72
Numerical Computing with Python
Understand, explore, and effectively present data using the powerful data visualization techniques of PythonKey FeaturesUse the power of Pandas and Matplotlib to easily solve data mining issuesUnderstand the basics of statistics to build powerful predictive data modelsGrasp data mining concepts with helpful use-cases and examplesBook DescriptionData mining, or parsing the data to extract useful insights, is a niche skill that can transform your career as a data scientist Python is a flexible programming language that is equipped with a strong suite of libraries and toolkits, and gives you the perfect platform to sift through your data and mine the insights you seek. This Learning Path is designed to familiarize you with the Python libraries and the underlying statistics that you need to get comfortable with data mining.You will learn how to use Pandas, Python's popular library to analyze different kinds of data, and leverage the power of Matplotlib to generate appealing and impressive visualizations for the insights you have derived. You will also explore different machine learning techniques and statistics that enable you to build powerful predictive models.By the end of this Learning Path, you will have the perfect foundation to take your data mining skills to the next level and set yourself on the path to become a sought-after data science professional.This Learning Path includes content from the following Packt products:Statistics for Machine Learning by Pratap DangetiMatplotlib 2.x By Example by Allen Yu, Claire Chung, Aldrin YimPandas Cookbook by Theodore PetrouWhat you will learnUnderstand the statistical fundamentals to build data modelsSplit data into independent groups Apply aggregations and transformations to each groupCreate impressive data visualizationsPrepare your data and design models Clean up data to ease data analysis and visualizationCreate insightful visualizations with Matplotlib and SeabornCustomize the model to suit your own predictive goalsWho this book is forIf you want to learn how to use the many libraries of Python to extract impactful information from your data and present it as engaging visuals, then this is the ideal Learning Path for you. Some basic knowledge of Python is enough to get started with this Learning Path.
R 1,628.00
Programming 3D Applications with HTML5 and WebGL
Create high-performance, visually stunning 3D applications for the Web, using HTML5 and related technologies such as CSS3 and WebGL - the emerging web graphics standard. With this book, you'll learn how to use the tools, frameworks, and libraries for building 3D models and animations, mind-blowing visual effects, and advanced user interaction in both desktop and mobile browsers. In two parts - Foundations and Application Development Techniques - author Tony Parisi provides a thorough grounding in theory and practice for designing everything from a simple 3D product viewer to immersive games and interactive training systems. Ideal for developers with Javascript and HTML experience. Explore HTML5 APIs and related technologies for creating 3D web graphics, including WebGL, Canvas, and CSS Work with the popular JavaScript 3D rendering and animation libraries Three.js and Tween.js Delve into the 3D content creation pipeline, and the modeling and animation tools for creating killer 3D content Look into several game engines and frameworks for building 3D applications, including the author's Vizi framework Create 3D environments with multiple objects and complex interaction, using examples and supporting code Examine the issues involved in building WebGL-based 3D applications for mobile browsers
R 1,557.00
Programming with Python
Based on the latest version of the language, this book offers a self-contained, concise and coherent introduction to programming with Python. The book’s primary focus is on realistic case study applications of Python. Each practical example is accompanied by a brief explanation of the problem-terminology and concepts, followed by necessary program development in Python using its constructs, and simulated testing. Given the open and participatory nature of development, Python has a variety of incorporated data structures, which has made it difficult to present it in a coherent manner. Further, some advanced concepts (super, yield, generator, decorator, etc.) are not easy to explain. The book specially addresses these challenges; starting with a minimal subset of the core, it offers users a step-by-step guide to achieving proficiency.
R 6,953.00
Data Science Projects with Python
Gain hands-on experience with industry-standard data analysis and machine learning tools in PythonKey FeaturesTackle data science problems by identifying the problem to be solvedIllustrate patterns in data using appropriate visualizationsImplement suitable machine learning algorithms to gain insights from dataBook DescriptionData Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You’ll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you’ll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions.By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.What you will learnInstall the required packages to set up a data science coding environmentLoad data into a Jupyter notebook running PythonUse Matplotlib to create data visualizationsFit machine learning models using scikit-learnUse lasso and ridge regression to regularize your modelsCompare performance between models to find the best outcomesUse k-fold cross-validation to select model hyperparametersWho this book is forIf you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful.
R 1,335.00
R 1,621.75
IoT Machine Learning Applications in Telecom, Energy, and Agriculture
Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. What You Will LearnImplement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with PythonSet up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenariosDevelop solutions for commercial-grade IoT or IIoT projectsImplement case studies in machine learning with IoT from scratchWho This Book Is ForRaspberry Pi and Arduino enthusiasts and data science and machine learning professionals.
R 1,555.49
Modelling Foundations and Applications
This book constitutes the proceedings of the 12th European Conference on Modelling Foundations and Applications, ECMFA 2016, held as part of STAF 2016, in Vienna, Austria, in July 2016. The 16 papers presented in this volume were carefully reviewed and selected from 47 submissions. The committee decided to accept 16 papers, 12 papers for the Foundations Track and 4 papers for the Applications Track. Papers on a wide range of MBE aspects were accepted, including topics such as multi- and many models, language engineering, UML and meta-modeling, experience reports and case studies, and variability and uncertainty.
R 2,171.09
Statistics and Calculus with Python Workshop
With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easyKey FeaturesDiscover how most programmers use the main Python libraries when performing statistics with PythonUse descriptive statistics and visualizations to answer business and scientific questionsSolve complicated calculus problems, such as arc length and solids of revolution using derivatives and integralsBook DescriptionAre you looking to start developing artificial intelligence applications? Do you need a refresher on key mathematical concepts? Full of engaging practical exercises, The Statistics and Calculus with Python Workshop will show you how to apply your understanding of advanced mathematics in the context of Python.The book begins by giving you a high-level overview of the libraries you'll use while performing statistics with Python. As you progress, you'll perform various mathematical tasks using the Python programming language, such as solving algebraic functions with Python starting with basic functions, and then working through transformations and solving equations. Later chapters in the book will cover statistics and calculus concepts and how to use them to solve problems and gain useful insights. Finally, you'll study differential equations with an emphasis on numerical methods and learn about algorithms that directly calculate values of functions.By the end of this book, you'll have learned how to apply essential statistics and calculus concepts to develop robust Python applications that solve business challenges.What you will learnGet to grips with the fundamental mathematical functions in PythonPerform calculations on tabular datasets using pandasUnderstand the differences between polynomials, rational functions, exponential functions, and trigonometric functionsUse algebra techniques for solving systems of equationsSolve real-world problems with probabilitySolve optimization problems with derivatives and integralsWho this book is forIf you are a Python programmer who wants to develop intelligent solutions that solve challenging business problems, then this book is for you. To better grasp the concepts explained in this book, you must have a thorough understanding of advanced mathematical concepts, such as Markov chains, Euler's formula, and Runge-Kutta methods as the book only explains how these techniques and concepts can be implemented in Python.
R 1,335.00
Knowledge Engineering, Machine Learning and Lattice Computing with Applications
This book constitutes the refereed proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2012, held in San Sebastian, Spain, in September 2012. The 20 revised full papers presented were carefully reviewed and selected from 130 submissions. The papers are organized in topical sections on bioinspired and machine learning methods, machine learning applications, semantics and ontology based techniques, and lattice computing and games.
R 1,969.11
R 855.00
Hands-on Time Series Analysis with Python
Learn the concepts of time series from traditional to bleeding-edge techniques. This book uses comprehensive examples to clearly illustrate statistical approaches and methods of analyzing time series data and its utilization in the real world. All the code is available in Jupyter notebooks.You'll begin by reviewing time series fundamentals, the structure of time series data, pre-processing, and how to craft the features through data wrangling. Next, you'll look at traditional time series techniques like ARMA, SARIMAX, VAR, and VARMA using trending framework like StatsModels and pmdarima. The book also explains building classification models using sktime, and covers advanced deep learning-based techniques like ANN, CNN, RNN, LSTM, GRU and Autoencoder to solve time series problem using Tensorflow. It concludes by explaining the popular framework fbprophet for modeling time series analysis. After reading Hands -On Time Series Analysis with Python, you'll be able to apply these new techniques in industries, such as oil and gas, robotics, manufacturing, government, banking, retail, healthcare, and more. What You'll Learn:· Explains basics to advanced concepts of time series· How to design, develop, train, and validate time-series methodologies· What are smoothing, ARMA, ARIMA, SARIMA,SRIMAX, VAR, VARMA techniques in time series and how to optimally tune parameters to yield best results· Learn how to leverage bleeding-edge techniques such as ANN, CNN, RNN, LSTM, GRU, Autoencoder to solve both Univariate and multivariate problems by using two types of data preparation methods for time series.· Univariate and multivariate problem solving using fbprophet. Who This Book Is ForData scientists, data analysts, financial analysts, and stock market researchers
R 1,254.00
Building Energy Modeling with OpenStudio
This textbook teaches the fundamentals of building energy modeling and analysis using open source example applications built with the US DOE’s OpenStudio modeling platform and EnergyPlus simulation engine. Designed by researchers at US National Laboratories to support a new generation of high performance buildings, EnergyPlus and OpenStudio are revolutionizing how building energy modeling is taught in universities and applied by professional architects and engineers around the world. The authors, all researchers at National Renewable Energy Laboratory and members of the OpenStudio software development team, present modeling concepts using open source software that may be generally applied using a variety of software tools commonly used by design professionals. The book also discusses modeling process automation in the context of OpenStudio Measures—small self-contained scripts that can transform energy models and their data—to save time and effort. They illustrate key concepts through a sophisticated example problem that evolves in complexity throughout the book. The text also examines advanced topics including daylighting, parametric analysis, uncertainty analysis, design optimization, and model calibration. Building Energy Modeling with OpenStudio teaches students to become sophisticated modelers rather than simply proficient software users. It supports undergraduate and graduate building energy courses in Architecture, and in Mechanical, Civil, Architectural, and Sustainability Engineering.
R 3,154.39
Derivatives Analytics with Python
Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourselfApply Fourier transform techniques and advanced Monte Carlo pricingCalibrate advanced option pricing models to market dataIntegrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python — Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts.
R 3,419.00
Building Flickr Applications with PHP
This is the first book that shows how to take advantage of both your photos and Flickr's enormous cache, to create new and compelling extensions to the Flickr platform. You’ll discover how to take advantage of open source tools (like PHP, Apache, and MySQL) and the Flickr architecture to manage, retrieve, and format photos in imaginative ways. You’ll also learn how to build upon Flickr's photo collaboration features to create interfaces for working with others on photo album projects. And you will learn how to take advantage of RSS to distribute photo updates with ease.
R 1,486.27
Database Systems for Advanced Applications
This two-volume set LNCS 11446 and LNCS 11447 constitutes the refereed proceedings of the 24th International Conference on Database Systems for Advanced Applications, DASFAA 2019, held in Chiang Mai, Thailand, in April 2019.The 92 full papers and 64 short papers were carefully selected from a total of 501 submissions. In addition, 13 demo papers and 6 tutorial papers are included. The full papers are organized in the following topics: big data; clustering and classification; crowdsourcing; data integration; embedding; graphs; knowledge graph; machine learning; privacy and graph; recommendation; social network; spatial; and spatio-temporal. The short papers, demo papers, and tutorial papers can be found in the volume LNCS 11448, which also includes the workshops of DASFAA 2019.
R 4,355.23
OpenCV 3.x with Python By Example
Learn the techniques for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications using examples on different functions of OpenCV.Key Features Learn how to apply complex visual effects to images with OpenCV 3.x and Python Extract features from an image and use them to develop advanced applications Build algorithms to help you understand image content and perform visual searches Get to grips with advanced techniques in OpenCV such as machine learning, artificial neural network, 3D reconstruction, and augmented realityBook DescriptionComputer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we have more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Focusing on OpenCV 3.x and Python 3.6, this book will walk you through all the building blocks needed to build amazing computer vision applications with ease.We start off by manipulating images using simple filtering and geometric transformations. We then discuss affine and projective transformations and see how we can use them to apply cool advanced manipulations to your photos like resizing them while keeping the content intact or smoothly removing undesired elements. We will then cover techniques of object tracking, body part recognition, and object recognition using advanced techniques of machine learning such as artificial neural network. 3D reconstruction and augmented reality techniques are also included. The book covers popular OpenCV libraries with the help of examples.This book is a practical tutorial that covers various examples at different levels, teaching you about the different functions of OpenCV and their actual implementation. By the end of this book, you will have acquired the skills to use OpenCV and Python to develop real-world computer vision applications.What you will learn Detect shapes and edges from images and videos How to apply filters on images and videos Use different techniques to manipulate and improve images Extract and manipulate particular parts of images and videos Track objects or colors from videos Recognize specific object or faces from images and videos How to create Augmented Reality applications Apply artificial neural networks and machine learning to improve object recognitionWho this book is forThis book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV and Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on.
R 1,661.00
Hands-On Meta Learning with Python
Explore a diverse set of meta-learning algorithms and techniques to enable human-like cognition for your machine learning models using various Python frameworksKey FeaturesUnderstand the foundations of meta learning algorithmsExplore practical examples to explore various one-shot learning algorithms with its applications in TensorFlowMaster state of the art meta learning algorithms like MAML, reptile, meta SGDBook DescriptionMeta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster.Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning.By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.What you will learnUnderstand the basics of meta learning methods, algorithms, and typesBuild voice and face recognition models using a siamese networkLearn the prototypical network along with its variantsBuild relation networks and matching networks from scratchImplement MAML and Reptile algorithms from scratch in PythonWork through imitation learning and adversarial meta learningExplore task agnostic meta learning and deep meta learningWho this book is forHands-On Meta Learning with Python is for machine learning enthusiasts, AI researchers, and data scientists who want to explore meta learning as an advanced approach for training machine learning models. Working knowledge of machine learning concepts and Python programming is necessary.
R 1,498.00
Building With Ethereum
Build products on top of Ethereum's new and expansive technological stack.Writing any good web application requires planning, care, and deft technical skills, but Ethereum's execution model presents its own challenges for engineers wishing to build applications on top of its smart contract layer. Building performant and engaging product experiences is one of the most important – and often underappreciated – roles in any company.This book looks at the full product stack needed to build such experiences on top of Ethereum smart contracts, weaving tutorials and case studies through more conversational discussions of the various constraints, trade-offs, and complexities involved in doing so. You’ll learn about the fundamentals of Ethereum from a new perspective, developing a strong understanding of how the Ethereum Virtual Machine (EVM) works and how it affects product engineering, as well as all the pieces of technology that go into decentralized apps (dapps) behind the front end: RPC nodes, wallets, indexers, application hosts, and more. You’ll be exposed to plenty of UI, JavaScript code, and idiomatic ways to bring on-chain data into your front ends. And you’ll be given up-to-date knowledge of the best practices and future possibilities that decentralized computation might offer the product engineer.What You Will LearnUnderstand the EVM and how it worksGain insight into smart contracts and how apps connect to themUnderstand the difference between live data and indexed dataHow decentralization affects the UI of applicationsBuild engaging, tasteful product experiences on top of EthereumWho This Book Is ForA confident – mid-level or senior – software engineer or web developer who hasn’t properly branched out into Ethereum; someone who might have scratched the surface, but wants a deeper understanding of the principles behind dapps, and who wants a head start on the hurdles faced while building them.
R 1,368.00
Introduction to Optimization with Applications in Machine Learning and Data Analytics
The primary goal of this text is a practical one. Equipping students with the enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors but also for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields.This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course.The book contains many practical applications but also explains the mathematics behind the techniques, including stating definitions and proving theorems. Optimization techniques are at the heart of the first spam filters, are used in self-driving cars, play a great role in machine learning, and can be used in such places as determining a batting order in a Major League Baseball game. Additionally, it has seemingly limitless other applications in business and industry.Many of the problems rely on using a computer. Microsoft’s Excel is most often used as this is common in business, but Python and other languages are considered. The consideration of other programming languages permits experienced mathematics and engineering students to use MATLAB or Mathematica or computer science students to write their own programs in Java or Python.
R 5,673.15
Build Production-Ready AI Agents with the Microsoft Stack
Build AI agents across Copilot Studio, Microsoft Foundry, and Agent Framework, from tenant setup and deployment to multimodal assessment, code-first workflows, evidence-based decisions, and production architecture choicesKey FeaturesBuild grounded, multimodal, and multi-agent solutions across three Microsoft agent tiersEvaluate, secure, publish, and monitor agents using production-focused workflowsImplement durable human-in-the-loop orchestration with checkpoints and auditable decisionsBook DescriptionMoving an AI agent from a working prototype to a production system requires more than a good prompt. You need the right architecture, reliable grounding, controlled tool use, evaluation, security, observability, and clear points for human intervention. Build Production-Ready AI Agents with the Microsoft Stack shows how these pieces fit together through through Claims 365, a fictional insurer's claims process.You will build a grounded business agent with Copilot Studio, secure and evaluate it, and publish it for real use. You will then create a multimodal assessment agent in Microsoft Foundry that processes images and documents and returns structured outputs. From there, you will run, test, and resume a complete code-first multi-agent workflow built with Microsoft Agent Framework, in Python or C#. It coordinates agents and tools to gather and challenge evidence, then pauses for durable human approval using checkpoints, shared state, Dataverse, and an auditable investigation trail.The book concludes by comparing the three Microsoft agent tiers (Copilot Studio, Microsoft Foundry, and Microsoft Agent Framework) and the trade-offs between flexibility, control, maintenance, and operational responsibility, helping you select the right architecture for different production scenarios.What you will learnChoose the right Microsoft agent tier for each workloadGround agent responses in Dataverse and business dataEvaluate grounded responses and identify failure casesSecure and publish agents with sign-in and entitlement checksBuild multimodal agents with structured JSON outputsDesign code-first multi-agent workflows with shared stateAdd durable human approval with checkpoints and audit trailsWho this book is forThis book is for solution architects, AI engineers, developers, technical consultants, and Microsoft platform professionals who need to take agentic solutions beyond prototypes. It is especially useful for readers working across Copilot Studio, Microsoft Foundry, and Microsoft Agent Framework who need to make architecture, security, evaluation, orchestration, and human-oversight decisions. Familiarity with Microsoft cloud services and application development will help readers get the most from the code-first sections.
R 1,140.00
Data Visualization with Python and JavaScript 2e
How do you turn raw, unprocessed, or malformed data into dynamic, interactive web visualizations? In this practical book, author Kyran Dale shows data scientists and analysts--as well as Python and JavaScript developers--how to create the ideal toolchain for the job. By providing engaging examples and stressing hard-earned best practices, this guide teaches you how to leverage the power of best-of-breed Python and JavaScript libraries. Python provides accessible, powerful, and mature libraries for scraping, cleaning, and processing data. And while JavaScript is the best language when it comes to programming web visualizations, its data processing abilities can't compare with Python's. Together, these two languages are a perfect complement for creating a modern web-visualization toolchain. This book gets you started. You'll learn how to: Obtain data you need programmatically, using scraping tools or web APIs: Requests, Scrapy, Beautiful Soup Clean and process data using Python's heavyweight data processing libraries within the NumPy ecosystem: Jupyter notebooks with pandas+Matplotlib+Seaborn Deliver the data to a browser with static files or by using Flask, the lightweight Python server, and a RESTful API Pick up enough web development skills (HTML, CSS, JS) to get your visualized data on the web Use the data you've mined and refined to create web charts and visualizations with Plotly, D3, Leaflet, and other libraries
R 1,505.00
Mathematical Modeling And Computation In Finance: With Exercises And Python And Matlab Computer Codes
This book discusses the interplay of stochastics (applied probability theory) and numerical analysis in the field of quantitative finance. The stochastic models, numerical valuation techniques, computational aspects, financial products, and risk management applications presented will enable readers to progress in the challenging field of computational finance.When the behavior of financial market participants changes, the corresponding stochastic mathematical models describing the prices may also change. Financial regulation may play a role in such changes too. The book thus presents several models for stock prices, interest rates as well as foreign-exchange rates, with increasing complexity across the chapters. As is said in the industry, 'do not fall in love with your favorite model.' The book covers equity models before moving to short-rate and other interest rate models. We cast these models for interest rate into the Heath-Jarrow-Morton framework, show relations between the different models, and explain a few interest rate products and their pricing.The chapters are accompanied by exercises. Students can access solutions to selected exercises, while complete solutions are made available to instructors. The MATLAB and Python computer codes used for most tables and figures in the book are made available for both print and e-book users. This book will be useful for people working in the financial industry, for those aiming to work there one day, and for anyone interested in quantitative finance. The topics that are discussed are relevant for MSc and PhD students, academic researchers, and for quants in the financial industry.Supplementary Material:Solutions Manual is available to instructors who adopt this textbook for their courses. Please contact sales@wspc.com.
R 2,125.00
