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Results 1131-1140 of 1488 (Search time: 0.068 seconds).
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  • Sách/Book

  • Java and Algorithmic Thinking for the Complete Beginner learn to think like a programmer

    • Authors: Aristides S. Bouras (2024)

  • What you will learn Understand the fundamentals of how computers work Master Java programming basics and IDEs Develop proficiency in handling operators, and trace tables Implement sequence and decision control structures in programming Manipulate numbers, strings, and complex expressions Utilize arrays, HashMaps, and other data structures effectively Who this book is for This course is perfect for complete beginners with no prior programming experience, including high school students and hobbyists. It is also suitable for those with a basic understanding of computers who wish to deepen their knowledge of Java and algorithmic thinking
  • Sách/Book

  • 100 java mistakes and how to avoid them.

    • Authors: Tagir Valeev (2024)

  • 100 Java Mistakes and How To Avoid Them highlights 100 Java coding errors—from beginner missteps to mistakes even Java experts don’t know they’re making. Each case includes clear examples to show you what to look for and concrete troubleshooting advice. You’ll learn to use static analysis tools like IntelliJ IDEA and SonarLint to ensure you’re consistently delivering exceptional Java, discover how unit tests and defensive coding can keep your code clean, and even learn to write your own bug-busting plugins
  • Sách/Book

  • Practical machine learning with R : tutorials and case studies

    • Authors: Carsten Lange (2024)

  • "This textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teach machine learning, emphasizing practical applications over complex mathematical concepts. Students are not required to have advanced mathematics knowledge such as matrix algebra or calculus. The author introduces machine learning algorithms, utilizing the widely used R language for statistical analysis. Each chapter includes examples, case studies, and interactive tutorials to enhance understanding. No prior programming knowledge is needed. The book leverages the tidymodels package, an extension of R, to streamline data processing and model workflows. This package simplifies commands, making the logic of algorit...
  • Sách/Book

  • Data structure and algorithm

    • Authors: Nikhat Raza Khan (2023)

  • Data structure and algorithms are two of the most important aspects of computer science. Data structure and algorithms help in understanding the nature of the problem at a deeper level and thereby a better understanding of the world. Learning data structure and algorithms will help you become a better programmer. This book provides a comprehensive introduction to the modern study of computer algorithms. It presents many algorithms and covers them in considerable depth, which makes their design and analysis accessible to all levels of readers. This book provides with an enjoyable introduction to the field of algorithms.
  • Sách/Book

  • JPA ( Java Persistence API ) For Beginner: Your Step-By-Step Guide For Beginner To Learn JPA Framework

    • Authors: Amit K (2024)

  • Welcome to 'JPA (Java Persistence API) For Beginner: Your Step-By-Step Guide For Beginner To Learn JPA Framework'. This book is meticulously crafted to serve as the ultimate starting point for anyone eager to dive into the world of Java Persistence API (JPA). Whether you are a novice in programming or an experienced coder wanting to extend your skills, this book will guide you through every aspect of JPA, ensuring that you grasp both basic and advanced concepts with ease and clarity.
  • Sách/Book

  • The machine learning solutions architect handbook

    • Authors: David Ping (2024)

  • You'll learn about ML algorithms, cloud infrastructure, system design, MLOps , and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You’ll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as ML risk management and the different stages of AI/ML adoption, the biggest new addition to the handbook is the deep exploration of generative AI.
  • Sách/Book

  • Ultimate Parallel and Distributed Computing with Julia for Data Science

    • Authors: Nabanita Dash (2024)

  • This book takes you through a step-by-step learning journey, starting with the essentials of Julia's syntax, variables, and functions. You'll unlock the power of efficient data handling by leveraging Julia arrays and DataFrames.jl for insightful analysis. Develop expertise in both basic and advanced statistical models, providing a robust toolkit for deriving meaningful data-driven insights. The journey continues with machine learning proficiency, where you'll
  • Sách/Book

  • Metaheuristics for machine learning : algorithms and applications

    • Authors: Kanak Kalita (2024)

  • This book arms you with the tools and understanding you need to tackle challenges efficiently. Equip yourself with this essential resource and navigate the ever-evolving landscape of machine learning and optimization with confidence. Audience The book is aimed at a broad audience encompassing researchers, practitioners, and students in the fields of computer science, data science, engineering, and mathematics. The detailed but accessible content makes it a must-have for both academia and industry professionals interested in the optimization aspects of machine learning algorithms
  • Sách/Book

  • Quantum machine learning : a modern approach

    • Authors: S. Karthikeyan (2024)

  • "This book presents the research into and application of machine learning in quantum computation, known as quantum machine learning (QML). It presents a comparison of quantum machine learning, classical machine learning, and traditional programming, along with the usage of quantum computing, towards improving traditional machine learning algorithms through case-studies. Covers the core and fundamental aspects of statistics, quantum learning and quantum machines Discusses the basics of machine learning, regression, supervised and un-supervised machine learning algorithms, and artificial neural networks Elaborates upon quantum machine learning models, quantum machine learning approaches and quantum classification, boosting. Introduces quantum evaluation models, deep quantum learning, ...