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| data science in rust: Practical Machine Learning with Rust Joydeep Bhattacharjee, 2019-12-10 Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you’ll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will Learn Write machine learning algorithms in RustUse Rust libraries for different tasks in machine learningCreate concise Rust packages for your machine learning applicationsImplement NLP and computer vision in RustDeploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust. |
| data science in rust: The Rust Programming Language (Covers Rust 2018) Steve Klabnik, Carol Nichols, 2019-09-03 The official book on the Rust programming language, written by the Rust development team at the Mozilla Foundation, fully updated for Rust 2018. The Rust Programming Language is the official book on Rust: an open source systems programming language that helps you write faster, more reliable software. Rust offers control over low-level details (such as memory usage) in combination with high-level ergonomics, eliminating the hassle traditionally associated with low-level languages. The authors of The Rust Programming Language, members of the Rust Core Team, share their knowledge and experience to show you how to take full advantage of Rust's features--from installation to creating robust and scalable programs. You'll begin with basics like creating functions, choosing data types, and binding variables and then move on to more advanced concepts, such as: Ownership and borrowing, lifetimes, and traits Using Rust's memory safety guarantees to build fast, safe programs Testing, error handling, and effective refactoring Generics, smart pointers, multithreading, trait objects, and advanced pattern matching Using Cargo, Rust's built-in package manager, to build, test, and document your code and manage dependencies How best to use Rust's advanced compiler with compiler-led programming techniques You'll find plenty of code examples throughout the book, as well as three chapters dedicated to building complete projects to test your learning: a number guessing game, a Rust implementation of a command line tool, and a multithreaded server. New to this edition: An extended section on Rust macros, an expanded chapter on modules, and appendixes on Rust development tools and editions. |
| data science in rust: Hands-On Data Analysis with Pandas Stefanie Molin, 2019-07-26 Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key FeaturesPerform efficient data analysis and manipulation tasks using pandasApply pandas to different real-world domains using step-by-step demonstrationsGet accustomed to using pandas as an effective data exploration toolBook Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling in PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning (ML) algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsUse pandas to solve common data representation and analysis problemsBuild Python scripts, modules, and packages for reusable analysis codeWho this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial. |
| data science in rust: Hands-On Data Structures and Algorithms with Rust Claus Matzinger, 2019-01-25 Design and implement professional level programs by exploring modern data structures and algorithms in Rust. Key FeaturesUse data structures such as arrays, stacks, trees, lists and graphs with real-world examplesLearn the functional and reactive implementations of the traditional data structuresExplore illustrations to present data structures and algorithms, as well as their analysis, in a clear, visual manner.Book Description Rust has come a long way and is now utilized in several contexts. Its key strengths are its software infrastructure and resource-constrained applications, including desktop applications, servers, and performance-critical applications, not forgetting its importance in systems' programming. This book will be your guide as it takes you through implementing classic data structures and algorithms in Rust, helping you to get up and running as a confident Rust programmer. The book begins with an introduction to Rust data structures and algorithms, while also covering essential language constructs. You will learn how to store data using linked lists, arrays, stacks, and queues. You will also learn how to implement sorting and searching algorithms. You will learn how to attain high performance by implementing algorithms to string data types and implement hash structures in algorithm design. The book will examine algorithm analysis, including Brute Force algorithms, Greedy algorithms, Divide and Conquer algorithms, Dynamic Programming, and Backtracking. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. What you will learnDesign and implement complex data structures in RustAnalyze, implement, and improve searching and sorting algorithms in RustCreate and use well-tested and reusable components with RustUnderstand the basics of multithreaded programming and advanced algorithm designBecome familiar with application profiling based on benchmarking and testingExplore the borrowing complexity of implementing algorithmsWho this book is for This book is for developers seeking to use Rust solutions in a practical/professional setting; who wants to learn essential Data Structures and Algorithms in Rust. It is for developers with basic Rust language knowledge, some experience in other programming languages is required. |
| data science in rust: Rust in Action Tim McNamara, 2021-09-07 This well-written book will help you make the most of what Rust has to offer. - Ramnivas Laddad, author of AspectJ in Action Rust in Action is a hands-on guide to systems programming with Rust. Written for inquisitive programmers, it presents real-world use cases that go far beyond syntax and structure. Summary Rust in Action introduces the Rust programming language by exploring numerous systems programming concepts and techniques. You'll be learning Rust by delving into how computers work under the hood. You'll find yourself playing with persistent storage, memory, networking and even tinkering with CPU instructions. The book takes you through using Rust to extend other applications and teaches you tricks to write blindingly fast code. You'll also discover parallel and concurrent programming. Filled to the brim with real-life use cases and scenarios, you'll go beyond the Rust syntax and see what Rust has to offer in real-world use cases. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Rust is the perfect language for systems programming. It delivers the low-level power of C along with rock-solid safety features that let you code fearlessly. Ideal for applications requiring concurrency, Rust programs are compact, readable, and blazingly fast. Best of all, Rust’s famously smart compiler helps you avoid even subtle coding errors. About the book Rust in Action is a hands-on guide to systems programming with Rust. Written for inquisitive programmers, it presents real-world use cases that go far beyond syntax and structure. You’ll explore Rust implementations for file manipulation, networking, and kernel-level programming and discover awesome techniques for parallelism and concurrency. Along the way, you’ll master Rust’s unique borrow checker model for memory management without a garbage collector. What's inside Elementary to advanced Rust programming Practical examples from systems programming Command-line, graphical and networked applications About the reader For intermediate programmers. No previous experience with Rust required. About the author Tim McNamara uses Rust to build data processing pipelines and generative art. He is an expert in natural language processing and data engineering. Table of Contents 1 Introducing Rust PART 1 RUST LANGUAGE DISTINCTIVES 2 Language foundations 3 Compound data types 4 Lifetimes, ownership, and borrowing PART 2 DEMYSTIFYING SYSTEMS PROGRAMMING 5 Data in depth 6 Memory 7 Files and storage 8 Networking 9 Time and timekeeping 10 Processes, threads, and containers 11 Kernel 12 Signals, interrupts, and exceptions |
| data science in rust: Data Science and Data Analytics Amit Kumar Tyagi, 2021-09-22 Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues. Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy. FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc. Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm’s productivity. |
| data science in rust: Rust for Rustaceans Jon Gjengset, 2021-12-21 Master professional-level coding in Rust. For developers who’ve mastered the basics, this book is the next step on your way to professional-level programming in Rust. It covers everything you need to build and maintain larger code bases, write powerful and flexible applications and libraries, and confidently expand the scope and complexity of your projects. Author Jon Gjengset takes you deep into the Rust programming language, dissecting core topics like ownership, traits, concurrency, and unsafe code. You’ll explore key concepts like type layout and trait coherence, delve into the inner workings of concurrent programming and asynchrony with async/await, and take a tour of the world of no_std programming. Gjengset also provides expert guidance on API design, testing strategies, and error handling, and will help develop your understanding of foreign function interfaces, object safety, procedural macros, and much more. You'll Learn: How to design reliable, idiomatic, and ergonomic Rust programs based on best principles Effective use of declarative and procedural macros, and the difference between them How asynchrony works in Rust – all the way from the Pin and Waker types used in manual implementations of Futures, to how async/await saves you from thinking about most of those words What it means for code to be unsafe, and best practices for writing and interacting with unsafe functions and traits How to organize and configure more complex Rust projects so that they integrate nicely with the rest of the ecosystem How to write Rust code that can interoperate with non-Rust libraries and systems, or run in constrained and embedded environments Brimming with practical, pragmatic insights that you can immediately apply, Rust for Rustaceans helps you do more with Rust, while also teaching you its underlying mechanisms. |
| data science in rust: Data Science and Analytics Sneha Kumari, K.K. Tripathy, Vidya Kumbhar, 2020-12-04 Data Science and Analytics explores the application of big data and business analytics by academics, researchers, industrial experts, policy makers and practitioners, helping the reader to understand how big data can be efficiently utilized in better managerial applications. |
| data science in rust: Speed Up Your Python with Rust Maxwell Flitton, 2022-01-21 Discover how to inject your code with highly performant Rust features to develop fast and memory-safe applications Key FeaturesLearn to implement Rust in a Python system without altering the entire systemWrite safe and efficient Rust code as a Python developer by understanding the essential features of RustBuild Python extensions in Rust by using Python NumPy modules in your Rust codeBook Description Python has made software development easier, but it falls short in several areas including memory management that lead to poor performance and security. Rust, on the other hand, provides memory safety without using a garbage collector, which means that with its low memory footprint, you can build high-performant and secure apps relatively easily. However, rewriting everything in Rust can be expensive and risky as there might not be package support in Rust for the problem being solved. This is where Python bindings and pip come in. This book will help you, as a Python developer, to start using Rust in your Python projects without having to manage a separate Rust server or application. Seeing as you'll already understand concepts like functions and loops, this book covers the quirks of Rust such as memory management to code Rust in a productive and structured manner. You'll explore the PyO3 crate to fuse Rust code with Python, learn how to package your fused Rust code in a pip package, and then deploy a Python Flask application in Docker that uses a private Rust pip module. Finally, you'll get to grips with advanced Rust binding topics such as inspecting Python objects and modules in Rust. By the end of this Rust book, you'll be able to develop safe and high-performant applications with better concurrency support. What you will learnExplore the quirks of the Rust programming language that a Python developer needs to understand to code in RustUnderstand the trade-offs for multiprocessing and thread safety to write concurrent codeBuild and manage a software project with cargo and cratesFuse Rust code with Python so that Python can import and run Rust codeDeploy a Python Flask application in Docker that utilizes a private Rust pip moduleInspect and create your own Python objects in RustWho this book is for This book is for Python developers who want to speed up their Python code with Rust and implement Rust in a Python system without altering the entire system. You'll be able to learn about all topics relating to Rust programming. Basic knowledge of Python is required to get the most out of this book. |
| data science in rust: Classic Computer Science Problems in Java David Kopec, 2020-12-21 Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. Summary Sharpen your coding skills by exploring established computer science problems! Classic Computer Science Problems in Java challenges you with time-tested scenarios and algorithms. You’ll work through a series of exercises based in computer science fundamentals that are designed to improve your software development abilities, improve your understanding of artificial intelligence, and even prepare you to ace an interview. As you work through examples in search, clustering, graphs, and more, you'll remember important things you've forgotten and discover classic solutions to your new problems! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Whatever software development problem you’re facing, odds are someone has already uncovered a solution. This book collects the most useful solutions devised, guiding you through a variety of challenges and tried-and-true problem-solving techniques. The principles and algorithms presented here are guaranteed to save you countless hours in project after project. About the book Classic Computer Science Problems in Java is a master class in computer programming designed around 55 exercises that have been used in computer science classrooms for years. You’ll work through hands-on examples as you explore core algorithms, constraint problems, AI applications, and much more. What's inside Recursion, memoization, and bit manipulation Search, graph, and genetic algorithms Constraint-satisfaction problems K-means clustering, neural networks, and adversarial search About the reader For intermediate Java programmers. About the author David Kopec is an assistant professor of Computer Science and Innovation at Champlain College in Burlington, Vermont. Table of Contents 1 Small problems 2 Search problems 3 Constraint-satisfaction problems 4 Graph problems 5 Genetic algorithms 6 K-means clustering 7 Fairly simple neural networks 8 Adversarial search 9 Miscellaneous problems 10 Interview with Brian Goetz |
| data science in rust: Python for Geeks Muhammad Asif, 2021-10-20 Take your Python skills to the next level to develop scalable, real-world applications for local as well as cloud deployment Key FeaturesAll code examples have been tested with Python 3.7 and Python 3.8 and are expected to work with any future 3.x releaseLearn how to build modular and object-oriented applications in PythonDiscover how to use advanced Python techniques for the cloud and clustersBook Description Python is a multipurpose language that can be used for multiple use cases. Python for Geeks will teach you how to advance in your career with the help of expert tips and tricks. You'll start by exploring the different ways of using Python optimally, both from the design and implementation point of view. Next, you'll understand the life cycle of a large-scale Python project. As you advance, you'll focus on different ways of creating an elegant design by modularizing a Python project and learn best practices and design patterns for using Python. You'll also discover how to scale out Python beyond a single thread and how to implement multiprocessing and multithreading in Python. In addition to this, you'll understand how you can not only use Python to deploy on a single machine but also use clusters in private as well as in public cloud computing environments. You'll then explore data processing techniques, focus on reusable, scalable data pipelines, and learn how to use these advanced techniques for network automation, serverless functions, and machine learning. Finally, you'll focus on strategizing web development design using the techniques and best practices covered in the book. By the end of this Python book, you'll be able to do some serious Python programming for large-scale complex projects. What you will learnUnderstand how to design and manage complex Python projectsStrategize test-driven development (TDD) in PythonExplore multithreading and multiprogramming in PythonUse Python for data processing with Apache Spark and Google Cloud Platform (GCP)Deploy serverless programs on public clouds such as GCPUse Python to build web applications and application programming interfacesApply Python for network automation and serverless functionsGet to grips with Python for data analysis and machine learningWho this book is for This book is for intermediate-level Python developers in any field who are looking to build their skills to develop and manage large-scale complex projects. Developers who want to create reusable modules and Python libraries and cloud developers building applications for cloud deployment will also find this book useful. Prior experience with Python will help you get the most out of this book. |
| data science in rust: Beginning Rust Programming Ric Messier, 2021-03-09 Quickly learn the ropes with the Rust programming language using this practical, step-by-step guide In Beginning Rust Programming, accomplished programmer and author Ric Messier delivers a highly practical, real-world guide to coding with Rust. Avoiding dry, theoretical content and “Hello, world”-type tutorials of questionable utility, the book dives immediately into functional Rust programming that takes advantage of the language’s blazing speed and memory efficiency. Designed from the ground up to give you a running start to using the multiparadigm system programming language, this book will teach you to: Solve real-world computer science problems of practical importance Use Rust’s rich type system and ownership model to guarantee memory-safety and thread-safety Integrate Rust with other programming languages and use it for embedded devices Perfect for programmers with some experience in other languages, like C or C++, Beginning Rust Programming is also a great pick for students new to programming and seeking a user-friendly and robust language with which to start their coding career. |
| data science in rust: Data Science and Artificial Intelligence Chutiporn Anutariya, |
| data science in rust: Programming Rust Jim Blandy, Jason Orendorff, 2017-11-21 Rust is a new systems programming language that combines the performance and low-level control of C and C++ with memory safety and thread safety. Rust’s modern, flexible types ensure your program is free of null pointer dereferences, double frees, dangling pointers, and similar bugs, all at compile time, without runtime overhead. In multi-threaded code, Rust catches data races at compile time, making concurrency much easier to use. Written by two experienced systems programmers, this book explains how Rust manages to bridge the gap between performance and safety, and how you can take advantage of it. Topics include: How Rust represents values in memory (with diagrams) Complete explanations of ownership, moves, borrows, and lifetimes Cargo, rustdoc, unit tests, and how to publish your code on crates.io, Rust’s public package repository High-level features like generic code, closures, collections, and iterators that make Rust productive and flexible Concurrency in Rust: threads, mutexes, channels, and atomics, all much safer to use than in C or C++ Unsafe code, and how to preserve the integrity of ordinary code that uses it Extended examples illustrating how pieces of the language fit together |
| data science in rust: Data Science Thinking Longbing Cao, 2018-08-17 This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. |
| data science in rust: Scientific Computing with Python - Second Edition CLAUS. FUHRER, Olivier Verdier, Jan Erik Solem, 2021-07-23 Leverage this example-packed, comprehensive guide for all your Python computational needs Key Features: Learn the first steps within Python to highly specialized concepts Explore examples and code snippets taken from typical programming situations within scientific computing. Delve into essential computer science concepts like iterating, object-oriented programming, testing, and MPI presented in strong connection to applications within scientific computing. Book Description: Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing. What You Will Learn: Understand the building blocks of computational mathematics, linear algebra, and related Python objects Use Matplotlib to create high-quality figures and graphics to draw and visualize results Apply object-oriented programming (OOP) to scientific computing in Python Discover how to use pandas to enter the world of data processing Handle exceptions for writing reliable and usable code Cover manual and automatic aspects of testing for scientific programming Get to grips with parallel computing to increase computation speed Who this book is for: This book is for students with a mathematical background, university teachers designing modern courses in programming, data scientists, researchers, developers, and anyone who wants to perform scientific computation in Python. |
| data science in rust: Encyclopedia of Data Science and Machine Learning Wang, John, 2023-01-20 Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians. |
| data science in rust: Grokking Deep Learning Andrew W. Trask, 2019-01-23 Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide |
| data science in rust: Rust Programming Cookbook Claus Matzinger, 2019-10-18 Practical solutions to overcome challenges in creating console and web applications and working with systems-level and embedded code, network programming, deep neural networks, and much more. Key FeaturesWork through recipes featuring advanced concepts such as concurrency, unsafe code, and macros to migrate your codebase to the Rust programming language Learn how to run machine learning models with Rust Explore error handling, macros, and modularization to write maintainable codeBook Description Rust 2018, Rust's first major milestone since version 1.0, brings more advancement in the Rust language. The Rust Programming Cookbook is a practical guide to help you overcome challenges when writing Rust code. This Rust book covers recipes for configuring Rust for different environments and architectural designs, and provides solutions to practical problems. It will also take you through Rust's core concepts, enabling you to create efficient, high-performance applications that use features such as zero-cost abstractions and improved memory management. As you progress, you'll delve into more advanced topics, including channels and actors, for building scalable, production-grade applications, and even get to grips with error handling, macros, and modularization to write maintainable code. You will then learn how to overcome common roadblocks when using Rust for systems programming, IoT, web development, and network programming. Finally, you'll discover what Rust 2018 has to offer for embedded programmers. By the end of the book, you'll have learned how to build fast and safe applications and services using Rust. What you will learnUnderstand how Rust provides unique solutions to solve system programming language problemsGrasp the core concepts of Rust to develop fast and safe applicationsExplore the possibility of integrating Rust units into existing applications for improved efficiencyDiscover how to achieve better parallelism and security with RustWrite Python extensions in RustCompile external assembly files and use the Foreign Function Interface (FFI)Build web applications and services using Rust for high performanceWho this book is for The Rust cookbook is for software developers looking to enhance their knowledge of Rust and leverage its features using modern programming practices. Familiarity with Rust language is expected to get the most out of this book. |
| data science in rust: Mastering Marketing Data Science Iain Brown, 2024-04-26 Unlock the Power of Data: Transform Your Marketing Strategies with Data Science In the digital age, understanding the symbiosis between marketing and data science is not just an advantage; it's a necessity. In Mastering Marketing Data Science: A Comprehensive Guide for Today's Marketers, Dr. Iain Brown, a leading expert in data science and marketing analytics, offers a comprehensive journey through the cutting-edge methodologies and applications that are defining the future of marketing. This book bridges the gap between theoretical data science concepts and their practical applications in marketing, providing readers with the tools and insights needed to elevate their strategies in a data-driven world. Whether you're a master's student, a marketing professional, or a data scientist keen on applying your skills in a marketing context, this guide will empower you with a deep understanding of marketing data science principles and the competence to apply these principles effectively. Comprehensive Coverage: From data collection to predictive analytics, NLP, and beyond, explore every facet of marketing data science. Practical Applications: Engage with real-world examples, hands-on exercises in both Python & SAS, and actionable insights to apply in your marketing campaigns. Expert Guidance: Benefit from Dr. Iain Brown's decade of experience as he shares cutting-edge techniques and ethical considerations in marketing data science. Future-Ready Skills: Learn about the latest advancements, including generative AI, to stay ahead in the rapidly evolving marketing landscape. Accessible Learning: Tailored for both beginners and seasoned professionals, this book ensures a smooth learning curve with a clear, engaging narrative. Mastering Marketing Data Science is designed as a comprehensive how-to guide, weaving together theory and practice to offer a dynamic, workbook-style learning experience. Dr. Brown's voice and expertise guide you through the complexities of marketing data science, making sophisticated concepts accessible and actionable. |
| data science in rust: Rust Web Development Bastian Gruber, 2023-03-07 Create bulletproof, high-performance web apps and servers with Rust. In Rust Web Development you will learn: Handling the borrow checker in an asynchronous environment Learning the ingredients of an asynchronous Rust stack Creating web APIs and using JSON in Rust Graceful error handling Testing, tracing, logging, and debugging Deploying Rust applications Efficient database access Rust Web Development is a pragmatic, hands-on guide to creating server-based web applications with Rust. If you’ve designed web servers using Java, NodeJS, or PHP, you’ll instantly fall in love with the performance and development experience Rust delivers. Hit the ground running! Author Bastian Gruber’s sage advice makes it easy to start tackling complex problems with Rust. You’ll learn how to work efficiently using pure Rust, along with important Rust libraries such as tokio for async runtimes, warp for web servers and APIs, and reqwest to run external HTTP requests. About the technology If you’re sick of cookie-cutter web development tools that are slow, resource hungry, and unstable, Rust is the solution. Rust services deliver rock-solid safety guarantees, an amazing developer experience, and even a compiler that automatically prevents common mistakes! About the book Rust Web Development, teaches you to build server-side web apps using Rust, along with important Rust libraries like tokio for async runtimes, warp for web servers and APIs, and reqwest to run external HTTP requests. The book is packed full of examples, code samples, and pro tips for setting up your projects and organizing your code. As you go, you’ll build a complete Q&A web service and iterate on your code chapter-by-chapter, just like a real development project. What's inside Handle the borrow checker in an asynchronous environment Build web APIs and handle JSON Compose a tech stack for asynchronous Rust development Handle errors gracefully Test, trace, log, and debug Deploy Rust applications to multiple environments About the reader This book is for web developers familiar with Java, Node, or Go, and the absolute basics of Rust. About the author Bastian Gruber was part of the official Rust Async Working Group, and founded the Rust and Tell Berlin MeetUp group. Table of Contents PART 1 INTRODUCTION TO RUST 1 Why Rust? 2 Laying the foundation PART 2 GETTING STARTED 3 Create your first route handler 4 Implement a RESTful API 5 Clean up your codebase 6 Logging, tracing, and debugging 7 Add a database to your application 8 Integrate third-party APIs PART 3 BRING IT INTO PRODUCTION 9 Add authentication and authorization 10 Deploy your application 11 Testing your Rust application |
| data science in rust: Rust Jonathan Waldman, 2015 Originally publlished in hardcover in 2015 by Simon & Schuster. |
| data science in rust: LEARN RUST Diego Rodrigues, 2024-10-31 Welcome to LEARN RUST: Secure and High-Performance Systems - 2024 Edition, the ultimate guide to mastering one of today's most promising programming languages. Written by Diego Rodrigues, a renowned author with over 180 titles published in six languages, this book offers a practical and structured approach to learning Rust and its applications in developing modern, secure, and highly performant systems. Whether you're a beginner or an experienced professional, this comprehensive manual covers everything from setting up your development environment to the most advanced Rust concepts, such as safe memory management, concurrency, and WebAssembly usage. Rust, designed to prevent critical memory errors and maximize performance, is already being adopted by tech giants like Google, Microsoft, and Amazon to build security infrastructures and critical applications. You'll learn to apply Rust in various contexts, such as embedded systems, cybersecurity, and high-performance web development. This book delves into topics like building scalable systems with microservices, functional programming with closures and iterators, and efficient data manipulation with custom types, along with optimizations for use on cloud platforms like AWS, Google Cloud, and Microsoft Azure. With practical examples and applied exercises at the end of each chapter, you'll have the opportunity to test your knowledge and develop robust, secure, and cloud-ready solutions for distributed systems. This is the ultimate resource for those looking to stand out in an increasingly competitive market by mastering one of the most powerful and relevant languages for the future. Prepare to take your programming skills to the next level with LEARN RUST: Secure and High-Performance Systems - 2024 Edition and become part of a global community shaping the future of technology. TAGS: Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes |
| data science in rust: Hands-on Rust Herbert Wolverson, 2021-06-30 Rust is an exciting new programming language combining the power of C with memory safety, fearless concurrency, and productivity boosters - and what better way to learn than by making games. Each chapter in this book presents hands-on, practical projects ranging from Hello, World to building a full dungeon crawler game. With this book, you'll learn game development skills applicable to other engines, including Unity and Unreal. Rust is an exciting programming language combining the power of C with memory safety, fearless concurrency, and productivity boosters. With Rust, you have a shiny new playground where your game ideas can flourish. Each chapter in this book presents hands-on, practical projects that take you on a journey from Hello, World to building a full dungeon crawler game. Start by setting up Rust and getting comfortable with your development environment. Learn the language basics with practical examples as you make your own version of Flappy Bird. Discover what it takes to randomly generate dungeons and populate them with monsters as you build a complete dungeon crawl game. Run game systems concurrently for high-performance and fast game-play, while retaining the ability to debug your program. Unleash your creativity with magical items, tougher monsters, and intricate dungeon design. Add layered graphics and polish your game with style. What You Need: A computer running Windows 10, Linux, or Mac OS X.A text editor, such as Visual Studio Code.A video card and drivers capable of running OpenGL 3.2. |
| data science in rust: Issues in Open Research Data Samuel A. Moore, 2014-12-19 n 2010 the Panton Principles for Open Data in Science were published. These principles were founded upon the idea that Science is based on building on, reusing and openly criticising the published body of scientific knowledge’ (http://pantonprinciples.org) and they provide a succinct list of the fundamentals to observe when making your data open. Intended for a broad audience of academics, publishers and librarians, Issues in Research Data explores the implications of the Panton Principles through a number of perspectives on open research data in the sciences and beyond. The book features chapters by open data experts in a range of academic disciplines, covering practical information on licensing, ethics, and advice for data curators, alongside more theoretical issues surrounding the adoption of open data. As the book is open access, each chapter can stand alone from the main volume so that communities can host, distribute, build upon and remix the content that is relevant to them. Readers can access the online version via the QR code or DOI link at the front of the book. |
| data science in rust: Learning Analytics Cookbook Roope Jaakonmäki, Jan vom Brocke, Stefan Dietze, Hendrik Drachsler, Albrecht Fortenbacher, René Helbig, Michael Kickmeier-Rust, Ivana Marenzi, Angel Suarez, Haeseon Yun, 2020-06-19 This book offers an introduction and hands-on examples that demonstrate how Learning Analytics (LA) can be used to enhance digital learning, teaching and training at various levels. While the majority of existing literature on the subject focuses on its application at large corporations, this book develops and showcases approaches that bring LA closer to smaller organizations, and to educational institutions that lack sufficient resources to implement a full-fledged LA infrastructure. In closing, the book introduces a set of software tools for data analytics and visualization, and explains how they can be employed in several LA scenarios. |
| data science in rust: Data Science from Scratch Joel Grus, 2015-04-14 Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases |
| data science in rust: Think Like a Data Scientist Brian Godsey, 2017-03-09 Summary Think Like a Data Scientist presents a step-by-step approach to data science, combining analytic, programming, and business perspectives into easy-to-digest techniques and thought processes for solving real world data-centric problems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Data collected from customers, scientific measurements, IoT sensors, and so on is valuable only if you understand it. Data scientists revel in the interesting and rewarding challenge of observing, exploring, analyzing, and interpreting this data. Getting started with data science means more than mastering analytic tools and techniques, however; the real magic happens when you begin to think like a data scientist. This book will get you there. About the Book Think Like a Data Scientist teaches you a step-by-step approach to solving real-world data-centric problems. By breaking down carefully crafted examples, you'll learn to combine analytic, programming, and business perspectives into a repeatable process for extracting real knowledge from data. As you read, you'll discover (or remember) valuable statistical techniques and explore powerful data science software. More importantly, you'll put this knowledge together using a structured process for data science. When you've finished, you'll have a strong foundation for a lifetime of data science learning and practice. What's Inside The data science process, step-by-step How to anticipate problems Dealing with uncertainty Best practices in software and scientific thinking About the Reader Readers need beginner programming skills and knowledge of basic statistics. About the Author Brian Godsey has worked in software, academia, finance, and defense and has launched several data-centric start-ups. Table of Contents PART 1 - PREPARING AND GATHERING DATA AND KNOWLEDGE Philosophies of data science Setting goals by asking good questions Data all around us: the virtual wilderness Data wrangling: from capture to domestication Data assessment: poking and prodding PART 2 - BUILDING A PRODUCT WITH SOFTWARE AND STATISTICS Developing a plan Statistics and modeling: concepts and foundations Software: statistics in action Supplementary software: bigger, faster, more efficient Plan execution: putting it all together PART 3 - FINISHING OFF THE PRODUCT AND WRAPPING UP Delivering a product After product delivery: problems and revisions Wrapping up: putting the project away |
| data science in rust: Modern C++ Design Andrei Alexandrescu, 2001 This title documents a convergence of programming techniques - generic programming, template metaprogramming, object-oriented programming and design patterns. It describes the C++ techniques used in generic programming and implements a number of industrial strength components. |
| data science in rust: Creating Value with Big Data Analytics Peter C. Verhoef, Edwin Kooge, Natasha Walk, 2016-01-08 Our newly digital world is generating an almost unimaginable amount of data about all of us. Such a vast amount of data is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organisations to leverage the information to create value. This book is a refreshingly practical, yet theoretically sound roadmap to leveraging big data and analytics. Creating Value with Big Data Analytics provides a nuanced view of big data development, arguing that big data in itself is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. By tying data and analytics to specific goals and processes for implementation, this is a much-needed book that will be essential reading for students and specialists of data analytics, marketing research, and customer relationship management. |
| data science in rust: Pragmatic AI Noah Gift, 2018-07-12 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| data science in rust: Creating Value with Data Analytics in Marketing Peter C. Verhoef, Edwin Kooge, Natasha Walk, Jaap E. Wieringa, 2021-11-07 This book is a refreshingly practical yet theoretically sound roadmap to leveraging data analytics and data science. The vast amount of data generated about us and our world is useless without plans and strategies that are designed to cope with its size and complexity, and which enable organizations to leverage the information to create value in marketing. Creating Value with Data Analytics in Marketing provides a nuanced view of big data developments and data science, arguing that big data is not a revolution but an evolution of the increasing availability of data that has been observed in recent times. Building on the authors’ extensive academic and practical knowledge, this book aims to provide managers and analysts with strategic directions and practical analytical solutions on how to create value from existing and new big data. The second edition of this bestselling text has been fully updated in line with developments in the field and includes a selection of new, international cases and examples, exercises, techniques and methodologies. Tying data and analytics to specific goals and processes for implementation makes this essential reading for advanced undergraduate and postgraduate students and specialists of data analytics, marketing research, marketing management and customer relationship management. Online resources include chapter-by-chapter lecture slides and data sets and corresponding R code for selected chapters. |
| data science in rust: Pandas for Everyone Daniel Y. Chen, 2017-12-15 The Hands-On, Example-Rich Introduction to Pandas Data Analysis in Python Today, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world problems. Chen gives you a jumpstart on using Pandas with a realistic dataset and covers combining datasets, handling missing data, and structuring datasets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes. Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability, and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export data Create plots with matplotlib, seaborn, and pandas Combine datasets and handle missing data Reshape, tidy, and clean datasets so they’re easier to work with Convert data types and manipulate text strings Apply functions to scale data manipulations Aggregate, transform, and filter large datasets with groupby Leverage Pandas’ advanced date and time capabilities Fit linear models using statsmodels and scikit-learn libraries Use generalized linear modeling to fit models with different response variables Compare multiple models to select the “best” Regularize to overcome overfitting and improve performance Use clustering in unsupervised machine learning |
| data science in rust: DATA SCIENCE Dr.Venkataramana Sarella, Mr. Sandeep Srivastava, Dr.K.Jamberi, Dr.Syed Khasim , 2022-05-01 DATA SCIENCE WRITTEN BY Dr.Venkataramana Sarella,Mr. Sandeep Srivastava, Dr.K.Jamberi, Dr.Syed Khasim |
| data science in rust: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry Chkoniya, Valentina, 2021-06-25 The contemporary world lives on the data produced at an unprecedented speed through social networks and the internet of things (IoT). Data has been called the new global currency, and its rise is transforming entire industries, providing a wealth of opportunities. Applied data science research is necessary to derive useful information from big data for the effective and efficient utilization to solve real-world problems. A broad analytical set allied with strong business logic is fundamental in today’s corporations. Organizations work to obtain competitive advantage by analyzing the data produced within and outside their organizational limits to support their decision-making processes. This book aims to provide an overview of the concepts, tools, and techniques behind the fields of data science and artificial intelligence (AI) applied to business and industries. The Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry discusses all stages of data science to AI and their application to real problems across industries—from science and engineering to academia and commerce. This book brings together practice and science to build successful data solutions, showing how to uncover hidden patterns and leverage them to improve all aspects of business performance by making sense of data from both web and offline environments. Covering topics including applied AI, consumer behavior analytics, and machine learning, this text is essential for data scientists, IT specialists, managers, executives, software and computer engineers, researchers, practitioners, academicians, and students. |
| data science in rust: Data Science and Interdisciplinary Research: Recent Trends and Applications Brojo Kishore Mishra, 2023-09-27 Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields. Key Themes: Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting. Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis. Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity. Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture. Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities. Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments. Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face. Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis. Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data. Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics. |
| data science in rust: Crafting Interpreters Robert Nystrom, 2021-07-27 Despite using them every day, most software engineers know little about how programming languages are designed and implemented. For many, their only experience with that corner of computer science was a terrifying compilers class that they suffered through in undergrad and tried to blot from their memory as soon as they had scribbled their last NFA to DFA conversion on the final exam. That fearsome reputation belies a field that is rich with useful techniques and not so difficult as some of its practitioners might have you believe. A better understanding of how programming languages are built will make you a stronger software engineer and teach you concepts and data structures you'll use the rest of your coding days. You might even have fun. This book teaches you everything you need to know to implement a full-featured, efficient scripting language. You'll learn both high-level concepts around parsing and semantics and gritty details like bytecode representation and garbage collection. Your brain will light up with new ideas, and your hands will get dirty and calloused. Starting from main(), you will build a language that features rich syntax, dynamic typing, garbage collection, lexical scope, first-class functions, closures, classes, and inheritance. All packed into a few thousand lines of clean, fast code that you thoroughly understand because you wrote each one yourself. |
| data science in rust: Programming Rust Jim Blandy, Jason Orendorff, Leonora F .S. Tindall, 2021-06-11 Systems programming provides the foundation for the world's computation. Writing performance-sensitive code requires a programming language that puts programmers in control of how memory, processor time, and other system resources are used. The Rust systems programming language combines that control with a modern type system that catches broad classes of common mistakes, from memory management errors to data races between threads. With this practical guide, experienced systems programmers will learn how to successfully bridge the gap between performance and safety using Rust. Jim Blandy, Jason Orendorff, and Leonora Tindall demonstrate how Rust's features put programmers in control over memory consumption and processor use by combining predictable performance with memory safety and trustworthy concurrency. You'll learn: Rust's fundamental data types and the core concepts of ownership and borrowing How to write flexible, efficient code with traits and generics How to write fast, multithreaded code without data races Rust's key power tools: closures, iterators, and asynchronous programming Collections, strings and text, input and output, macros, unsafe code, and foreign function interfaces This revised, updated edition covers the Rust 2021 Edition. |
| data science in rust: Data Science and Big Data Analytics Durgesh Mishra, |
| data science in rust: Mastering Rust Rahul Sharma, Vesa Kaihlavirta, 2019-01-31 Become proficient in designing, developing and deploying effective software systems using the advanced constructs of Rust Key FeaturesImprove your productivity using the latest version of Rust and write simpler and easier codeUnderstand Rust’s immutability and ownership principle, expressive type system, safe concurrencyDeep dive into the new doamins of Rust like WebAssembly, Networking and Command line toolsBook Description Rust is an empowering language that provides a rare combination of safety, speed, and zero-cost abstractions. Mastering Rust – Second Edition is filled with clear and simple explanations of the language features along with real-world examples, showing you how you can build robust, scalable, and reliable programs. This second edition of the book improves upon the previous one and touches on all aspects that make Rust a great language. We have included the features from latest Rust 2018 edition such as the new module system, the smarter compiler, helpful error messages, and the stable procedural macros. You’ll learn how Rust can be used for systems programming, network programming, and even on the web. You’ll also learn techniques such as writing memory-safe code, building idiomatic Rust libraries, writing efficient asynchronous networking code, and advanced macros. The book contains a mix of theory and hands-on tasks so you acquire the skills as well as the knowledge, and it also provides exercises to hammer the concepts in. After reading this book, you will be able to implement Rust for your enterprise projects, write better tests and documentation, design for performance, and write idiomatic Rust code. What you will learnWrite generic and type-safe code by using Rust’s powerful type system How memory safety works without garbage collection Know the different strategies in error handling and when to use themLearn how to use concurrency primitives such as threads and channels Use advanced macros to reduce boilerplate code Create efficient web applications with the Actix-web frameworkUse Diesel for type-safe database interactions in your web applicationWho this book is for The book is aimed at beginner and intermediate programmers who already have familiarity with any imperative language and have only heard of Rust as a new language. If you are a developer who wants to write robust, efficient and maintainable software systems and want to become proficient with Rust, this book is for you. It starts by giving a whirlwind tour of the important concepts of Rust and covers advanced features of the language in subsequent chapters using code examples that readers will find useful to advance their knowledge. |
The Rust Programming Language - Stanford University
Appendix A covers Rust’s keywords, Appendix B covers Rust’s operators and symbols, Appendix C covers derivable traits provided by the standard library, Appendix D covers some useful …
DATA ANALYSIS W ITH RUST NOTEBOOKS - Data Crayon
t, I've decided to try Rust again. This time, I'm going to write a book which focusses on using Rust and Jupyter Notebooks to implement algorithms and conduct experiments, most likely in the …
Rust in the data science and machine learning stack
Give a brief overview of the Rust programming language. Study the cases of polars, tokenizers, pydantic, ruff and uv, analyzing the main reasons for their success. Rust is a great alternative …
Data Science With Rust: A Comprehensive Guide - Data …
compile-time checks on data races and other concurrency errors, Rust empowers developers to write concurrent code that is both safe and efficient - a challenging feat in traditional system …
Ivan Smirnov - Hierarchical Data Format
"Rust is a multi-paradigm system programming language focused on safety, especially safe concurrency." "Rust is syntactically similar to C++, but is designed to provide better memory …
CSE 410 - Advanced Data Structures Topic 02: Rust Basics
Language Basics // Define an immutable variable let my_var = "the thing"; // Define a mutable variable let m ut my_var = "the thing"; // Define a function fn my_fun(arg1: type1, arg2: type_2) …
Raphtory: The temporal graph engine for Rust and Python
the sheer volume of temporal network data, which often dwarfs that of an equivalent static network. The library provides an expressive Python interface for interoperability with other data …
High-Performance Computation on a Rust-based distributed …
Based on our experiences, we present a Rust-based ABM engine capable of distributing models on high-performance computing resources, gaining remarkable speedup against the …
Mostly Painless Scientific Computing With Rust - SC22
In this poster, we document our experience in developing Rusty Tree, and use it as a tool to explore the current landscape of scientific computing with Rust. From multithreading, and …
Rust For Data Science - crm.hilltimes.com
Rust For Data Science: Practical Machine Learning with Rust Joydeep Bhattacharjee,2019-12-10 Explore machine learning in Rust and learn about the intricacies of creating machine learning …
Practical Machine Learning with Rust - Springer
Feb 28, 2019 · This book is all about exploring Machine Learning in Rust lang. We will learn about the intricacies of creating machine learning applications and how they fit in the Rust worldview. …
Translating C to Safe Rust - seas.upenn.edu
implementing a data structure often taught at first year has now become an Internet trope. A number of solutions have been proposed for these problems, including incorporating a …
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING …
Understand and apply Rust’s object-oriented features such as traits, trait objects, and design patterns for polymorphism. LIST OF EXPERIMENTS: Module-I: Key features of Rust, …
Visualization of Reference Lifetimes in Rust - ETH Zürich
The goal of this project is to build a tool that will help Rust developers to reason about lifetimes and related compiler errors, by visualizing lifetimes and (optionally) constraints between them. …
High Performance Document Store Implementation in Rust
help to understand about how search engines work, their requirements for data persistence and retrieval, and characteristic functionalities. This chapter will cover, in brief, the evolution of …
WHY SCIENTISTS ARE TURNING TO RUST - Nature
Workflows for analysing scientific data tend to use languages such as Python, R and Matlab. These interpret lines of code one by one and then execute them, a style of programming that …
Rust For Data Science (2024) - crm.hilltimes.com
Rust For Data Science: Practical Machine Learning with Rust Joydeep Bhattacharjee,2019-12-10 Explore machine learning in Rust and learn about the intricacies of creating machine learning …
OSS Scripting System for Game Development in Rust
Amethyst is an open-source game engine in Rust, which, currently, does not have a scripting system. Therefore, it requires expertise in Rust to develop a game within its framework. In …
The World’s Most Popular Data Science Platform - Anaconda
In Anaconda’s 2021 State of Data Science report, 63% of respondents said they use Python frequently or always, making it the most popular language of those included in the survey.
Rust For Data Science - crm.hilltimes.com
Rust For Data Science: R for Data Science Hadley Wickham,Garrett Grolemund,2016-12-12 Learn how to use R to turn raw data into insight knowledge and understanding This book …
The Rust Programming Language - Stanford University
Appendix A covers Rust’s keywords, Appendix B covers Rust’s operators and symbols, Appendix C covers derivable traits provided by the standard library, Appendix D covers some useful …
DATA ANALYSIS W ITH RUST NOTEBOOKS - Data Crayon
t, I've decided to try Rust again. This time, I'm going to write a book which focusses on using Rust and Jupyter Notebooks to implement algorithms and conduct experiments, most likely in the …
Rust in the data science and machine learning stack
Give a brief overview of the Rust programming language. Study the cases of polars, tokenizers, pydantic, ruff and uv, analyzing the main reasons for their success. Rust is a great alternative …
Data Science With Rust: A Comprehensive Guide - Data …
compile-time checks on data races and other concurrency errors, Rust empowers developers to write concurrent code that is both safe and efficient - a challenging feat in traditional system …
Ivan Smirnov - Hierarchical Data Format
"Rust is a multi-paradigm system programming language focused on safety, especially safe concurrency." "Rust is syntactically similar to C++, but is designed to provide better memory …
CSE 410 - Advanced Data Structures Topic 02: Rust Basics
Language Basics // Define an immutable variable let my_var = "the thing"; // Define a mutable variable let m ut my_var = "the thing"; // Define a function fn my_fun(arg1: type1, arg2: type_2) …
Raphtory: The temporal graph engine for Rust and Python
the sheer volume of temporal network data, which often dwarfs that of an equivalent static network. The library provides an expressive Python interface for interoperability with other data …
High-Performance Computation on a Rust-based distributed …
Based on our experiences, we present a Rust-based ABM engine capable of distributing models on high-performance computing resources, gaining remarkable speedup against the …
Mostly Painless Scientific Computing With Rust - SC22
In this poster, we document our experience in developing Rusty Tree, and use it as a tool to explore the current landscape of scientific computing with Rust. From multithreading, and …
Rust For Data Science - crm.hilltimes.com
Rust For Data Science: Practical Machine Learning with Rust Joydeep Bhattacharjee,2019-12-10 Explore machine learning in Rust and learn about the intricacies of creating machine learning …
Practical Machine Learning with Rust - Springer
Feb 28, 2019 · This book is all about exploring Machine Learning in Rust lang. We will learn about the intricacies of creating machine learning applications and how they fit in the Rust worldview. …
Translating C to Safe Rust - seas.upenn.edu
implementing a data structure often taught at first year has now become an Internet trope. A number of solutions have been proposed for these problems, including incorporating a …
DEPARTMENT OF COMPUTER SCIENCE AND …
Understand and apply Rust’s object-oriented features such as traits, trait objects, and design patterns for polymorphism. LIST OF EXPERIMENTS: Module-I: Key features of Rust, …
Visualization of Reference Lifetimes in Rust - ETH Zürich
The goal of this project is to build a tool that will help Rust developers to reason about lifetimes and related compiler errors, by visualizing lifetimes and (optionally) constraints between them. …
High Performance Document Store Implementation in Rust
help to understand about how search engines work, their requirements for data persistence and retrieval, and characteristic functionalities. This chapter will cover, in brief, the evolution of …
WHY SCIENTISTS ARE TURNING TO RUST - Nature
Workflows for analysing scientific data tend to use languages such as Python, R and Matlab. These interpret lines of code one by one and then execute them, a style of programming that …
Rust For Data Science (2024) - crm.hilltimes.com
Rust For Data Science: Practical Machine Learning with Rust Joydeep Bhattacharjee,2019-12-10 Explore machine learning in Rust and learn about the intricacies of creating machine learning …
OSS Scripting System for Game Development in Rust
Amethyst is an open-source game engine in Rust, which, currently, does not have a scripting system. Therefore, it requires expertise in Rust to develop a game within its framework. In …
The World’s Most Popular Data Science Platform - Anaconda
In Anaconda’s 2021 State of Data Science report, 63% of respondents said they use Python frequently or always, making it the most popular language of those included in the survey.
Rust For Data Science - crm.hilltimes.com
Rust For Data Science: R for Data Science Hadley Wickham,Garrett Grolemund,2016-12-12 Learn how to use R to turn raw data into insight knowledge and understanding This book introduces …
