Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods. This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you'll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you'll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script. Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work. What You Will Learn Discover how changes in hyperparameters affect the model's performance. Apply different hyperparameter tuning algorithms to data science problems Work with Bayesian optimization methods to create efficient machine learning and deep learning models Distribute hyperparameter optimization using a cluster of machines Approach automated machine learning using hyperparameter optimization Who This Book Is For Professionals and students working with machine learning.

The C programming language is a popular language in industries as well as academics. Since its invention and standardized as ANSI C, several other standards known as C99, C11, and C17 were published with new features in subsequent years. This book covers all the traits of ANSI C and includes new features present in other standards. The content of this book helps a beginner to learn the fundamental concept of the C language. The book contains a step-by-step explanation of every program that allows a learner to understand the syntax and builds a foundation to write similar programs. The explanation clarity, exercises, and illustrations present in this book make it a complete textbook in all aspects. Features: Other than ANSI C, the book explains the new C standards like C99, C11, and C17. Most basic and easy-to-follow programs are chosen to explain the concepts and their syntax. More emphasis is given to the topics like Functions, Pointers, and Structures. Recursion is emphasized with numerous programming examples and diagrams. A separate chapter on the command-line argument and preprocessors is included that concisely explains their usage. Several real-life figures are taken to explain the concepts of dynamic memory allocation, file handling, and the difference between structure and union. The book contains more than 260 illustrations, more than 200 programs, and exercises at the end of each chapter. This book serves as a textbook for UG/PG courses in science and engineering. The researcher, postgraduate engineers, and embedded software developers can also keep this book as reference material for their fundamental learning.

Survival analysis uses statistics to calculate time to failure. Survival Analysis with Python takes a fresh look at this complex subject by explaining how to use the Python programming language to perform this type of analysis. As the subject itself is very mathematical and full of expressions and formulations, the book provides detailed explanations and examines practical implications. The book begins with an overview of the concepts underpinning statistical survival analysis. It then delves into Parametric models with coverage of Concept of maximum likelihood estimate (MLE) of a probability distribution parameter MLE of the survival function Common probability distributions and their analysis Analysis of exponential distribution as a survival function Analysis of Weibull distribution as a survival function Derivation of Gumbel distribution as a survival function from Weibull Non-parametric models including Kaplan-Meier (KM) estimator, a derivation of expression using MLE Fitting KM estimator with an example dataset, Python code and plotting curves Greenwood's formula and its derivation Models with covariates explaining The concept of time shift and the accelerated failure time (AFT) model Weibull-AFT model and derivation of parameters by MLE Proportional Hazard (PH) model Cox-PH model and Breslow's method Significance of covariates Selection of covariates The Python lifelines library is used for coding examples. By mapping theory to practical examples featuring datasets, this book is a hands-on tutorial as well as a handy reference.

For the first time microcontrollers are powerful enough to be programmed in Python. The landscape of embedded systems development is changing, microcontrollers are becoming more powerful, and the rise of the internet of things is leading more developers to get into hardware. This book provides the solid foundation to start your journey of embedded systems development and microcontroller programming with Python. You'll quickly realize the value of using Python. The theme of the book is simplicity and the cleanness and elegance of Python makes that possible. Featuring a step-by-step approach, this single source guide balances complexity and clarity with insightful explanations that you'll easily grasp. Python is quickly becoming the language of choice for applications such as machine learning and computer vision on embedded devices. What would previously be daunting and exceedingly difficult to do in C or C++ is now possible with Python because of its level of abstraction. Programming Microcontrollers with Python is your path to bringing your existing skills to the embedded space. What You'll Learn Review microcontroller basics and the hardware and software requirements Understand an embedded system's general architecture Follow the steps needed to carry a product to market Take a crash course in Python programming Program a microcontroller Interface with a microcontroller using LCD and Circuit Python Use and control sensors Who This Book Is For Those getting started with microcontrollers, those new to C, C++, and Arduino programming, web developers looking to get into IoT, or Python programmers who wish to control hardware devices.

Description With Kubernetes, users don't have to worry about which specific machine in their data center their application is running on. Each layer in their application is decoupled from other layers so they can scale, update, and maintain them independently. Kubernetes in Action teaches developers how to use Kubernetes to deploy self-healing scalable distributed applications. By the end, readers will be able to build and deploy applications in a proper way to take full advantage of the Kubernetes platform. Key features * Easy to follow guide * Hands-on examples * Clearly-written Audience The book is for both application developers as well as system administrators who want to learn about Kubernetes from the developer's perspective. About the Technology Kubernetes abstracts away the hardware infrastructure and exposes your whole datacenter as a single enormous computational resource.

SQL is the ubiquitous language for software developers working with structured data. Most developers who rely on SQL are experts in their favorite language (such as Java, Python, or Go), but they're not experts in SQL. They often depend on antipatterns - solutions that look right but become increasingly painful to work with as you uncover their hidden costs. Learn to identify and avoid many of these common blunders. Refactor an inherited nightmare into a data model that really works. Updated for the current versions of MySQL and Python, this new edition adds a dozen brand new mini-antipatterns for quick wins. No matter which platform, framework, or language you use, the database is the foundation of your application, and the SQL database language is the standard for working with it. Antipatterns are solutions that look simple at the surface, but soon mire you down with needless work. Learn to identify these traps, and craft better solutions for the often-asked questions in this book. Avoid the mistakes that lead to poor performance and quality, and master the principles that make SQL a powerful and flexible tool for handling data and logic. Dive deep into SQL and database design, and learn to recognize the most common missteps made by software developers in database modeling, SQL query logic, and code design of data-driven applications. See practical examples of misconceptions about SQL that can lure software projects astray. Find the greatest value in each group of data. Understand why an intersection table may be your new best friend. Store passwords securely and don't reinvent the wheel. Handle NULL values like a pro. Defend your web applications against the security weakness of SQL injection. Use SQL the right way - it can save you from headaches and needless work, and let your application really shine! What You Need: The SQL examples use the MySQL 8.0 flavor, but other popular brands of RDBMS are mentioned. Other code examples use Python 3.9+ or Ruby 2.7+.

Bring together machine learning (ML) and deep learning (DL) in financial trading, with an emphasis on investment management. This book explains systematic approaches to investment portfolio management, risk analysis, and performance analysis, including predictive analytics using data science procedures. The book introduces pattern recognition and future price forecasting that exerts effects on time series analysis models, such as the Autoregressive Integrated Moving Average (ARIMA) model, Seasonal ARIMA (SARIMA) model, and Additive model, and it covers the Least Squares model and the Long Short-Term Memory (LSTM) model. It presents hidden pattern recognition and market regime prediction applying the Gaussian Hidden Markov Model. The book covers the practical application of the K-Means model in stock clustering. It establishes the practical application of the Variance-Covariance method and Simulation method (using Monte Carlo Simulation) for value at risk estimation. It also includes market direction classification using both the Logistic classifier and the Multilayer Perceptron classifier. Finally, the book presents performance and risk analysis for investment portfolios. By the end of this book, you should be able to explain how algorithmic trading works and its practical application in the real world, and know how to apply supervised and unsupervised ML and DL models to bolster investment decision making and implement and optimize investment strategies and systems. What You Will Learn Understand the fundamentals of the financial market and algorithmic trading, as well as supervised and unsupervised learning models that are appropriate for systematic investment portfolio management Know the concepts of feature engineering, data visualization, and hyperparameter optimization Design, build, and test supervised and unsupervised ML and DL models Discover seasonality, trends, and market regimes, simulating a change in the market and investment strategy problems and predicting market direction and prices Structure and optimize an investment portfolio with preeminent asset classes and measure the underlying risk Who This Book Is For Beginning and intermediate data scientists, machine learning engineers, business executives, and finance professionals (such as investment analysts and traders)

Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes. The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways. What You Will Learn Build, train, and deploy machine learning models at scale using Kubernetes Containerize any kind of machine learning model and run it on any platform using Docker Deploy machine learning and deep learning models using Flask and Streamlit frameworks Who This Book Is For Data engineers, data scientists, analysts, and machine learning and deep learning engineers

Go on an adventure and build a roguelike from scratch using JavaScript. With the help of the battle-tested Phaser library, you'll go through all the steps to build a small, fun, playable web roguelite game. The author will guide you on how to add further features to the game such as populating the game with enemies, adding treasures, and so on. You will acquire technical knowledge about procedural generation and tile-based mapping as well as learn game design skills such as what makes dungeons fun and how to evoke an emotion in your game. Roguelikes are very popular with indie developers because of their focus on gameplay over graphics. You'll see why they appeal to game designers on a budget and discover that they serve as a good platform to experiment with novel ideas and designs. Along the way, you'll cover the increasingly popular roguelite genre that provides a hyper casual form of the genre that is approachable and often mobile. After reading this book, you'll be ready to create your own roguelikes, to dive deep into procedural generation, and also to bring some of the techniques shown here into other genres and game projects. What You Will Learn Make use of procedural generation for dungeons, mazes, monsters, and treasure Pick up skills to use Phaser to build games Implement turn-based mechanics Use tile-based graphics Who This Book Is For Game developers who want to build something fun and who have at least some prior JavaScript programming experience.

Work with Db2 to write SQL and access databases using optimized code for the fastest response. This book will give you complete documentation on DB2 via Python for the IBM_db module and provide a number of examples for the usage of each module API. Begin by getting your free version of Db2 for Linux and Windows. While the book concentrates more on the Linux version of Db2, it also covers enough of the Windows version so that you're comfortable with obtaining and installing Db2 on your version of Windows. Next, you'll see how to install the sample database that comes with Db2, and take some data from the web to design a database around it, including tables and indexes. For Db2 to be really useful you need to use strong SQL expressions. This book provides specific examples of how to avoid using poor ones that can cause extra processing time for the query. Lastly, you'll look at each API in the ibm_db and ibm_db_dbi module. This module is not sponsored by IBM and must be installed separately from the Db2 database. After reading Foundation Db2 and Python you'll be able to install Db2 on Windows or Linux, and perform backups and restore data. What You'll Learn Obtain and install Db2 properly on Linux and Windows Create databases and load them on Db2 Work with ibm_db and ibm_db_dbi API modules for Python Write SQL for Db2 Review the future of the ibm_db Python module Who This Book Is For Python programmers and DB2 administrators interested in building application with Python and DB2

C Primer Plus is a carefully tested, well-crafted, and complete tutorial on a subject core to programmers and developers. This computer science classic teaches principles of programming, including structured code and top-down design. Author and educator Stephen Prata has created an introduction to C that is instructive, clear, and insightful. Fundamental programming concepts are explained along with details of the C language. Many short, practical examples illustrate just one or two concepts at a time, encouraging readers to master new topics by immediately putting them to use. Review questions and programming exercises at the end of each chapter bring out the most critical pieces of information and help readers understand and digest the most difficult concepts. A friendly and easy-to-use self-study guide, this book is appropriate for serious students of programming, as well as developers proficient in other languages with a desire to better understand the fundamentals of this core language. The sixth edition of this book has been updated and expanded to cover the latest developments in C as well as to take a detailed look at the new C11 standard. In C Primer Plus you'll find depth, breadth, and a variety of teaching techniques and tools to enhance your learning: Complete, integrated discussion of both C language fundamentals and additional features Clear guidance about when and why to use different parts of the language Hands-on learning with concise and simple examples that develop your understanding of a concept or two at a time Hundreds of practical sample programs Review questions and programming exercises at the end of each chapter to test your understanding Coverage of generic C to give you the greatest flexibility

Do you have an awesome idea for the next break-through mobile gaming title? This updated edition will help you kick-start your project as it guides you through the process of creating several example game apps using APIs available in Android. You will learn the basics needed to join the ranks of successful Android game app developers. the book starts with game design fundamentals using Canvas and Android SDK 10 or earlier programming basics. You then will progress toward creating your own basic game engine and playable game apps that work on Android 10 or earlier smartphones and tablets. You take your game through the chapters and topics in the book to learn different tools such as OpenGL ES. And you will learn about publishing and marketing your games to monetize your creation. What You Will Learn Gain knowledge on the fundamentals of game programming in the context of Android Use Android's APIs for graphics, audio, and user input to reflect those fundamentals Develop two 2D games from scratch, based on Canvas API and OpenGL ES Create a full-featured 3D game Publish your games, get crash reports, and support your users Complete your own playable 2D OpenGL games Who This Book Is For Those with basic knowledge of Java who want to write games on the Android platform, and experienced game developers who want to know about the pitfalls and peculiarities of the platform

Quickly gain the insight necessary to address a multitude of Java-based Spring Framework and Spring Boot coding challenges using this succinct reference guide. Short, focused code examples will help you master many of the Spring elements such as AOP, data access, MVC for web development, security, web services/REST for microservices development, Batch for large data sets, and more. You'll also get a rundown of the increasingly popular Spring Boot microframework as well. You won't find any technical jargon, bloated samples, drawn out history lessons, or witty stories in this book. What you will find is a language reference that is concise, to the point, and highly accessible. The book is packed with useful information and is a must-have for any Java programmer. What You Will Learn Discover the Spring Framework and its many sub-projects Work with the Spring Boot microframework Program with the Spring Boot framework to build Java-based microservices, native cloud applications, and more Use some of the lesser-known but still important frameworks and tools such as REST Docs, HATEOAS, Cloud, and more Leverage these Spring frameworks and tools for building your next Java-based cloud application Who This Book Is For Those with prior experience with Java who want a quick and handy reference to using Spring.

Gain the skills to begin developing Raku applications from the ground up in this hands-on compact book, which includes a foreword from Larry Wall, creator of Perl. You'll learn enough to get started building with Raku, using Raku's gradual typing, handy object orientated features, powerful parsing capabilities, and human-usable concurrency. This book has been updated to include the latest version of Raku based upon the Perl 6.d major version which includes over 3,400 new commits in its specification. After a short introduction, each chapter develops a small example project, explaining the Raku features used. When the example is done, you'll explore another aspect, such as optimizing further for readability or testing the code. Along the way you'll see Raku basics, such as variables and scoping; subroutines; classes and objects; regexes; and code testing. When you've mastered the basics, Raku Fundamentals moves onto more advanced topics to give you a deeper understanding of the language. You'll learn, amongst other things, how to work with persistent storage, how to generate good error messages, and how to write tricky applications such as a file and directory usage graph and a Unicode search tool. What You Will Learn Get coding with latest version of Raku Work on several hands-on examples and projects Integrate Python libraries into your Raku-based programs Parse INI files using regexes and grammars Build a date-time converter Carry out refactoring and other automated tests Who This Book Is For If you already know one or more programming languages, and want to learn about Raku, then this book is for you.

Master the C code appropriate for numerical methods and computational modeling, including syntax, loops, subroutines, and files. Then, this hands-on book dives into financial applications using regression models, product moment correlation coefficients, and asset pricing. Next, Practical Numerical C Programming covers applications for engineering/business such as supermarket stock reordering simulation as well as flight information boards at airports and controlling a power plant. Finally, the book concludes with some physics including building simulation models for energy and pendulum motion. Along the way, you'll learn center-of-mass calculations, Brownian motion, and more. After reading and using this book, you'll come away with pragmatic case studies of actual applications using C code at work. Source code is freely available and includes the latest C20 standard release. What You Will Learn Apply regression techniques to find the pattern for depreciation of the value of cars over a period of years Work with the product moment correlation coefficient technique to illustrate the accuracy (or otherwise) of regression techniques Use the past stock values of an asset to predict what its future values may be using Monte Carlo methods Simulate the buying of supermarket stock by shoppers and check the remaining stock: if it is too low print a message to reorder the stock Create a file of arrivals for an airport and send data to the airport's display boards to show the current situation for the incoming flights Simulate the patterns of particles moving in gases or solids Who This Book Is For Programmers and computational modelers with at least some prior experience with programming in C as well as programming in general.

Make the most of R's extensive toolset R Projects For Dummies offers a unique learn-by-doing approach. You will increase the depth and breadth of your R skillset by completing a wide variety of projects. By using R's graphics, interactive, and machine learning tools, you'll learn to apply R's extensive capabilities in an array of scenarios. The depth of the project experience is unmatched by any other content online or in print. And you just might increase your statistics knowledge along the way, too! R is a free tool, and it's the basis of a huge amount of work in data science. It's taking the place of costly statistical software that sometimes takes a long time to learn. One reason is that you can use just a few R commands to create sophisticated analyses. Another is that easy-to-learn R graphics enable you make the results of those analyses available to a wide audience. This book will help you sharpen your skills by applying them in the context of projects with R, including dashboards, image processing, data reduction, mapping, and more. Appropriate for R users at all levels Helps R programmers plan and complete their own projects Focuses on R functions and packages Shows how to carry out complex analyses by just entering a few commands If you're brand new to R or just want to brush up on your skills, R Projects For Dummies will help you complete your projects with ease.

* Covers all aspects of VB .NET programming in seven self-contained minibooks: Visual Basic .NET Programming Fundamentals, Advanced Visual Basic .NET Programming, The .NET Editor, Object-Oriented Programming, Programming for the Web, Database Programming, and Graphics and Games
* Visual Basic is the primary tool of more than fifty percent of all professional developers, so the upgrade to VB .NET represents a major paradigm shift; this handy all-in-one guide gives them easy access to valuable information
* Guides the reader through getting integrated with the rest of Visual Studio .NET, covers programmatic encryption and other .NET security capabilities, and shows how to program for Web services with VB .NET and ASP.NET
* Companion Web site includes a must-have bonus appendix that provides parallel VB 6 and VB .NET sample code to help VB programmers make the somewhat difficult transition to .NET

Maintaining a practical perspective, Python Programming: A Practical Approach acquaints you with the wonderful world of programming. The book is a starting point for those who want to learn Python programming. The backbone of any programming, which is the data structure and components such as strings, lists, etc., have been illustrated with many examples and enough practice problems to instill a level of self-confidence in the reader. Drawing on knowledge gained directly from teaching Computer Science as a subject and working on a wide range of projects related to ML, AI, deep learning, and blockchain, the authors have tried their best to present the necessary skills for a Python programmer. Once the foundation of Python programming is built and the readers are aware of the exact structure, dimensions, processing, building blocks, and representation of data, they can readily take up their specific problems from the area of interest and solve them with the help of Python. These include, but are not limited to, operators, control flow, strings, functions, module processing, object-oriented programming, exception and file handling, multithreading, synchronization, regular expressions, and Python database programming. This book on Python programming is specially designed to keep readers busy with learning fundamentals and generates a sense of confidence by attempting the assignment problems. We firmly believe that explaining any particular technology deviates from learning the fundamentals of a programming language. This book is focused on helping readers attempt implementation in their areas of interest through the skills imparted through this book. We have attempted to present the real essence of Python programming, which you can confidently apply in real life by using Python as a tool. Salient Features Based on real-world requirements and solution. Simple presentation without avoiding necessary details of the topic. Executable programs on almost every topic. Plenty of exercise questions, designed to test readers' skills and understanding. Purposefully designed to be instantly applicable, Python Programming: A Practical Approach provides implementation examples so that the described subject matter can be immediately implemented due to the well-known versatility of Python in handling different data types with ease.

Writing reliable and maintainable C++ software is hard. Designing such software at scale adds a new set of challenges. Creating large-scale systems requires a practical understanding of logical design - beyond the theoretical concepts addressed in most popular texts. To be successful on an enterprise scale, developers must also address physical design, a dimension of software engineering that may be unfamiliar even to expert developers. Drawing on over 30 years of hands-on experience building massive, mission-critical enterprise systems, John Lakos shows how to create and grow Software Capital. This groundbreaking volume lays the foundation for projects of all sizes and demonstrates the processes, methods, techniques, and tools needed for successful real-world, large-scale development. Up to date and with a solid engineering focus, Large-Scale C++, Volume I: Process and Architecture, demonstrates fundamental design concepts with concrete examples. Professional developers of all experience levels will gain insights that transform their approach to design and development by understanding how to Raise productivity by leveraging differences between infrastructure and application development Achieve exponential productivity gains through feedback and hierarchical reuse Embrace the component's role as the fundamental unit of both logical and physical design Analyze how fundamental properties of compiling and linking affect component design Discover effective partitioning of logical content in appropriately sized physical aggregates Internalize the important differences among sufficient, complete, minimal, and primitive software Deliver solutions that simultaneously optimize encapsulation, stability, and performance Exploit the nine established levelization techniques to avoid cyclic physical dependencies Use lateral designs judiciously to avoid the "heaviness" of conventional layered architectures Employ appropriate architectural insulation techniques for eliminating compile-time coupling Master the multidimensional process of designing large systems using component-based methods This is the first of John Lakos's three authoritative volumes on developing large-scale systems using C++. This book, written for fellow software practitioners, uses familiar C++ constructs to solve real-world problems while identifying (and motivating) modern C++ alternatives. Together with the forthcoming Volume II: Design and Implementation and Volume III: Verification and Testing, Large-Scale C++ offers comprehensive guidance for all aspects of large-scale C++ software development. If you are an architect or project leader, this book will empower you to solve critically important problems right now - and serve as your go-to reference for years to come. Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Use ontologies in Python, with the Owlready2 module developed for ontology-oriented programming. You will start with an introduction and refresher on Python and OWL ontologies. Then, you will dive straight into how to access, create, and modify ontologies in Python. Next, you will move on to an overview of semantic constructs and class properties followed by how to perform automatic reasoning. You will also learn about annotations, multilingual texts, and how to add Python methods to OWL classes and ontologies. Using medical terminologies as well as direct access to RDF triples is also covered. Python is one of the most used programming languages, especially in the biomedical field, and formal ontologies are also widely used. However, there are limited resources for the use of ontologies in Python. Owlready2, downloaded more than 60,000 times, is a response to this problem, and this book is the first one on the topic of using ontologies with Python. What You Will Learn Use Owlready2 to access and modify OWL ontologies in Python Publish ontologies on dynamic websites Perform automatic reasoning in Python Use well-known ontologies, including DBpedia and Gene Ontology, and terminological resources, such as UMLS (Unified Medical Language System) Integrate Python methods in OWL ontologies Who Is This Book For Beginner to experienced readers from biomedical sciences and artificial intelligence fields would find the book useful.

Gain a better understanding of pointers, from the basics of how pointers function at the machine level, to using them for a variety of common and advanced scenarios. This short contemporary guide book on pointers in C programming provides a resource for professionals and advanced students needing in-depth hands-on coverage of pointer basics and advanced features. It includes the latest versions of the C language, C20, C17, and C14. You'll see how pointers are used to provide vital C features, such as strings, arrays, higher-order functions and polymorphic data structures. Along the way, you'll cover how pointers can optimize a program to run faster or use less memory than it would otherwise. There are plenty of code examples in the book to emulate and adapt to meet your specific needs. What You Will Learn Work effectively with pointers in your C programming Learn how to effectively manage dynamic memory Program with strings and arrays Create recursive data structures Implement function pointers Who This Book Is For Intermediate to advanced level professional programmers, software developers, and advanced students or researchers. Prior experience with C programming is expected.

Understand and work with the most important features of advanced C# in different programming environments. This book teaches you the fundamental features of advanced C# and how to incorporate them in different programming techniques using Visual Studio 2019. The book is divided into two parts. Part I covers the fundamentals and essentials of advanced programming in C#. You will be introduced to delegates and events and then move on to lambda expressions. Part II teaches you how to implement these features in different programming techniques, starting with generic programming. After that, you will learn about thread programming and asynchronous programming, to benefit from a multi-threaded environment. Finally, you will learn database programming using ADO.NET to connect to a MySQL database and you will know how to exercise SQL statements and stored procedures through your C# applications. What You Will Learn Use delegates, events, and lambda expressions in advanced programming Make your application flexible by utilizing generics Create a fast application with multi-threading and asynchronous programming Work in Visual Studio Community Edition, which is the most common IDE for using C# Understand alternative implementations along with their pros and cons Who This Book Is For Developers and programmers who are already working in C#

Understand the newest trend in database programming for developers working in Java, Kotlin, Clojure, and other JVM-based languages. This book introduces Reactive Relational Database Connectivity (R2DBC), a modern way of connecting to and querying relational databases from Java and other JVM languages. The book begins by helping you understand not only what reactive programming is, but why it is necessary. Then building on those fundamentals, the book takes you into the world of databases and the newly released Reactive Relational Database Connectivity (R2DBC) specification. Examples in the book are worked using the freely available MariaDB database along with MariaDB's vendor-implementation of the R2DBC service-provider interface (SPI). Following along with the examples and the provided example code helps prepare you to work with any of the growing number of R2DBC implementations for popular enterprise databases such as Oracle Database and SQL Server. You'll be well prepared for what is becoming the future of database access from Java and other languages built on the JVM. What You Will Learn Understand why R2DBC was created and how it utilizes the Reactive Streams API Understand the components of the R2DBC service-provider interface Create and manage reactive database connections and connection pools using an R2DBC client Programmatically execute queries on a relational database using an R2DBC client Effectively utilize transactions using an R2DBC client Build relational database-driven applications that are event-driven and non-blocking Who This Book Is For Software developers building solutions using JVM languages and the JVM ecosystem, and developers who need an introduction to the R2DBC specification and reactive programming with relational databases and want to understand what Reactive Relational Database Connectivity is and why it came about. This book includes practical examples of using the R2DBC specification with Java and MariaDB that will provide developers with the knowledge they need to create their own solutions.

Use ensemble learning techniques and models to improve your machine learning results. Ensemble Learning for AI Developers starts you at the beginning with an historical overview and explains key ensemble techniques and why they are needed. You then will learn how to change training data using bagging, bootstrap aggregating, random forest models, and cross-validation methods. Authors Kumar and Jain provide best practices to guide you in combining models and using tools to boost performance of your machine learning projects. They teach you how to effectively implement ensemble concepts such as stacking and boosting and to utilize popular libraries such as Keras, Scikit Learn, TensorFlow, PyTorch, and Microsoft LightGBM. Tips are presented to apply ensemble learning in different data science problems, including time series data, imaging data, and NLP. Recent advances in ensemble learning are discussed. Sample code is provided in the form of scripts and the IPython notebook. What You Will Learn Understand the techniques and methods utilized in ensemble learning Use bagging, stacking, and boosting to improve performance of your machine learning projects by combining models to decrease variance, improve predictions, and reduce bias Enhance your machine learning architecture with ensemble learning Who This Book Is For Data scientists and machine learning engineers keen on exploring ensemble learning

Discover a variety of data-mining algorithms that are useful for selecting small sets of important features from among unwieldy masses of candidates, or extracting useful features from measured variables. As a serious data miner you will often be faced with thousands of candidate features for your prediction or classification application, with most of the features being of little or no value. You'll know that many of these features may be useful only in combination with certain other features while being practically worthless alone or in combination with most others. Some features may have enormous predictive power, but only within a small, specialized area of the feature space. The problems that plague modern data miners are endless. This book helps you solve this problem by presenting modern feature selection techniques and the code to implement them. Some of these techniques are: Forward selection component analysis Local feature selection Linking features and a target with a hidden Markov model Improvements on traditional stepwise selection Nominal-to-ordinal conversion All algorithms are intuitively justified and supported by the relevant equations and explanatory material. The author also presents and explains complete, highly commented source code. The example code is in C++ and CUDA C but Python or other code can be substituted; the algorithm is important, not the code that's used to write it. What You Will Learn Combine principal component analysis with forward and backward stepwise selection to identify a compact subset of a large collection of variables that captures the maximum possible variation within the entire set. Identify features that may have predictive power over only a small subset of the feature domain. Such features can be profitably used by modern predictive models but may be missed by other feature selection methods. Find an underlying hidden Markov model that controls the distributions of feature variables and the target simultaneously. The memory inherent in this method is especially valuable in high-noise applications such as prediction of financial markets. Improve traditional stepwise selection in three ways: examine a collection of 'best-so-far' feature sets; test candidate features for inclusion with cross validation to automatically and effectively limit model complexity; and at each step estimate the probability that our results so far could be just the product of random good luck. We also estimate the probability that the improvement obtained by adding a new variable could have been just good luck. Take a potentially valuable nominal variable (a category or class membership) that is unsuitable for input to a prediction model, and assign to each category a sensible numeric value that can be used as a model input. Who This Book Is For Intermediate to advanced data science programmers and analysts.