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Coding Interviews: Advanced Guide to Help You Excel at Coding Interviews
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Coding Interviews: Advanced Guide to Help You Excel at Coding InterviewsНазвание: Coding Interviews: Advanced Guide to Help You Excel at Coding Interviews
Автор: Logan Pratt
Издательство: Independently published
Год: 2022
Страниц: 180
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.2 MB

The ultimate guide to help you ace your coding interview! Coding interviews really are just interviews for a job, but they differ in that they are incredibly technical, and you need to know how to solve programs, turn your solutions into working code and make sure your solution is the best it can be – without making any mistakes. Coding interviews are typically several rounds, and each round needs its own specific preparation. You have no idea what questions you are likely to be asked, which is why I have written his guide. What is the best programming language to use in your coding interview? And does it even matter which one you use? The short answer to that is yes, it does matter. Most companies will allow you to use a programming language of your choice unless they use a specific one. The only real exception to that is Google, where candidates must choose between Python, Java, jаvascript, and C++. However, the language you choose can impact your performance more than you would believe possible. This is why choosing the right programming language at the start of your interview preparation is critical, as is using it regularly to get used to it.


Категория: КНИГИ » ПРОГРАММИРОВАНИЕ
Непрерывное развитие API. Правильные решения в изменчивом технологическом ландшафте, 2-е издание
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Непрерывное развитие API. Правильные решения в изменчивом технологическом ландшафте, 2-е издание
Название: Непрерывное развитие API. Правильные решения в изменчивом технологическом ландшафте, 2-е издание
Автор: Меджуи Мехди, Уайлд Эрик, Митра Ронни, Амундсен Майк
Издательство: Питер
Год: 2023
Формат: pdf
Страниц: 369
Размер: 17,7 Мб
Язык: русский

Для реализации API необходимо провести большую работу, но эти усилия не всегда окупаются. Чрезмерное планирование может стать пустой тратой сил, а его недостаток приводит к катастрофическим последствиям. Во втором издании представлены решения для отдельных API и систем из нескольких API, которые позволят вам распределить необходимые ресурсы и достичь требуемого уровня эффективности за оптимальное время. Как соблюсти баланс гибкости и производительности, сохранив надежность и простоту настройки? Четыре эксперта по API объясняют разработчикам, руководителям продуктов и проектов, как максимально увеличить ценность их API, управляя интерфейсами как продуктами с непрерывным жизненным циклом.


Категория: КНИГИ » ПРОГРАММИРОВАНИЕ
Курс программирования на языке Си
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Название: Курс программирования на языке Си
Автор: Подбельский В.В., Фомин С.С.
Издательство: ДМК Пресс
Год: 2012
Страниц: 384
ISBN: 978-5-94074-449-8
Формат: PDF
Размер: 11 Мб
Язык: русский

Настоящий учебник предназначен для изучения программирования на стандартном языке Си. Ориентация сделана как на изложение синтаксиса и семантики конструкций языка, так и на их практическое использование при решении типовых задач программирования.


Категория: КНИГИ » ПРОГРАММИРОВАНИЕ
Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python
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Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with PythonНазвание: Explainable AI Recipes: Implement Solutions to Model Explainability and Interpretability with Python
Автор: Pradeepta Mishra
Издательство: Apress
Год: 2023
Страниц: 267
Язык: английский
Формат: pdf, epub
Размер: 15.2 MB

Understand how to use Explainable AI (XAI) libraries and build trust in AI and machine learning models. This book utilizes a problem-solution approach to explaining Machine Learning models and their algorithms. The book starts with model interpretation for supervised learning linear models, which includes feature importance, partial dependency analysis, and influential data point analysis for both classification and regression models. Next, it explains supervised learning using non-linear models and state-of-the-art frameworks such as SHAP values/scores and LIME for local interpretation. Explainability for time series models is covered using LIME and SHAP, as are natural language processing-related tasks such as text classification, and sentiment analysis with ELI5, and ALIBI. The book concludes with complex model classification and regression-like neural networks and deep learning models using the CAPTUM framework that shows feature attribution, neuron attribution, and activation attribution. After reading this book, you will understand AI and Machine Learning models and be able to put that knowledge into practice to bring more accuracy and transparency to your analyses.


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Machine Learning and Deep Learning in Computational Toxicology
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Machine Learning and Deep Learning in Computational ToxicologyНазвание: Machine Learning and Deep Learning in Computational Toxicology
Автор: Huixiao Hong
Издательство: Springer
Год: 2023
Страниц: 654
Язык: английский
Формат: pdf (true)
Размер: 19.5 MB

This book is a collection of Machine Learning and Deep Learning algorithms, methods, architectures, and software tools that have been developed and widely applied in predictive toxicology. It compiles a set of recent applications using state-of-the-art Machine Learning and Deep Learning techniques in analysis of a variety of toxicological endpoint data. The contents illustrate those Machine Learning and Deep Learning algorithms, methods, and software tools and summarise the applications of Machine Learning and Deep Learning in predictive toxicology with informative text, figures, and tables that are contributed by the first tier of experts. One of the major features is the case studies of applications of Machine Learning and Deep Learning in toxicological research that serve as examples for readers to learn how to apply Machine Learning and Deep Learning techniques in predictive toxicology. This book is expected to provide a reference for practical applications of Machine Learning and Deep Learning in toxicological research.


Категория: КНИГИ » ПРОГРАММИРОВАНИЕ
Programming Languages: Build, Prove, and Compare
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Programming Languages: Build, Prove, and CompareНазвание: Programming Languages: Build, Prove, and Compare
Автор: Norman Ramsey
Издательство: Cambridge University Press
Год: 2023
Страниц: 800
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB

Computer scientists often need to learn new programming languages quickly. The best way to prepare for this is to understand the foundational principles that underlie even the most complicated industrial languages. This text for an undergraduate programming languages course distills great languages and their design principles down to easy-to-learn 'bridge' languages implemented by interpreters whose key parts are explained in the text. The book goes deep into the roots of both functional and object-oriented programming, and it shows how types and modules, including generics/polymorphism, contribute to effective programming. The book is not just about programming languages; it is also about programming. The ideas, descriptive techniques, and examples are conveyed by means of bridge languages. A bridge language models a real programming language, but it is small enough to describe formally and to learn in a week or two, yet big enough to write interesting programs in. The bridge languages in this book model Algol, Scheme, ML, CLU, and Smalltalk, and they are related to many modern descendants, including C, C++, OCaml, Haskell, Java, jаvascript, Python, Ruby, and Rust.


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Learning Modern C++ for Finance (Fourth Release)
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Learning Modern C++ for Finance (Fourth Release)Название: Learning Modern C++ for Finance: Foundations for Quantitative Programming (Fourth Release)
Автор: Daniel Hanson
Издательство: O’Reilly Media, Inc.
Год: 2023-02-08
Страниц: 204
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.18 MB

A lot of financial modeling has gravitated toward Python, R, and VBA, but many developers hit a wall with these languages when it comes to performance. This practical book demonstrates why C++ is still one of the dominant production-quality languages for financial applications and systems. Many programmers believe that C++ is too difficult to learn. Author Daniel Hanson demonstrates that this is no longer the case. Financial programmers coming from Python or another interpreted language will discover how to leverage C++ abstractions that enable safer and quicker implementation of financial models. You'll also explore how popular open source libraries provide additional weapons for attacking mathematical problems. C++ programmers unfamiliar with financial applications will also benefit from this handy guide.


Категория: КНИГИ » ПРОГРАММИРОВАНИЕ
Reinforcement Learning for Finance: Solve Problems in Finance with CNN and RNN Using the TensorFlow Library
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Reinforcement Learning for Finance: Solve Problems in Finance with CNN and RNN Using the TensorFlow LibraryНазвание: Reinforcement Learning for Finance: Solve Problems in Finance with CNN and RNN Using the TensorFlow Library
Автор: Samit Ahlawat
Издательство: Apress
Год: 2023
Страниц: 435
Язык: английский
Формат: pdf (true), epub (true)
Размер: 37.3 MB

This book introduces reinforcement learning with mathematical theory and practical examples from quantitative finance using the TensorFlow library. Reinforcement Learning for Finance begins by describing methods for training neural networks. Next, it discusses CNN and RNN – two kinds of neural networks used as deep learning networks in reinforcement learning. Further, the book dives into reinforcement learning theory, explaining the Markov decision process, value function, policy, and policy gradients, with their mathematical formulations and learning algorithms. It covers recent reinforcement learning algorithms from double deep-Q networks to twin-delayed deep deterministic policy gradients and generative adversarial networks with examples using the TensorFlow Python library. It also serves as a quick hands-on guide to TensorFlow programming, covering concepts ranging from variables and graphs to automatic differentiation, layers, models, and loss functions.


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Spark в действии: С примерами на Java, Python и Scala
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Название: Spark в действии: С примерами на Java, Python и Scala
Автор: Перрен Ж. -Ж.
Издательство: ДМК Пресс
Год: 2021
Cтраниц: 636, ил.
Формат: pdf
Размер: 15 мб
Язык: русский

Анализ корпоративных данных начинается с чтения, фильтрации и объединения файлов и потоков из многих источников. Механизм обработки данных Spark способен обрабатывать эти разнообразные объемы информации как признанный лидер в этой области, обеспечивая в 100 раз большую скорость, чем например Hadoop. Благодаря поддержке SQL, интуитивно понятному интерфейсу и простому и ясному многоязыковому API вы можете использовать Spark без глубокого изучения новой сложной экосистемы. Эта книга научит вас создавать полноценные и завершенные аналитические приложения. В качестве примера используется полный конвейер обработки данных, поступающих со спутников NASA. Для чтения этой книги не требуется какой-либо предварительный опыт работы со Spark, Scala или Hadoop.


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Fusion of Machine Learning Paradigms: Theory and Applications
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Fusion of Machine Learning Paradigms: Theory and ApplicationsНазвание: Fusion of Machine Learning Paradigms: Theory and Applications
Автор: Ioannis K. Hatzilygeroudis, George A. Tsihrintzis, Lakhmi C. Jain
Издательство: Springer
Год: 2023
Страниц: 204
Язык: английский
Формат: pdf (true), epub
Размер: 28.3 MB

This book aims at updating the relevant computer science-related research communities, including professors, researchers, scientists, engineers and students, as well as the general reader from other disciplines, on the most recent advances in applications of methods based on Fusing Machine Learning Paradigms. Integrated or Hybrid Machine Learning methodologies combine together two or more Machine Learning approaches achieving higher performance and better efficiency when compared to those of their constituent components and promising major impact in science, technology and the society. The book consists of an editorial note and an additional eight chapters and is organized into two parts, namely: (i) Recent Application Areas of Fusion of Machine Learning Paradigms and (ii) Applications that can clearly benefit from Fusion of Machine Learning Paradigms. One of the most successful and most promising Machine Learning approaches that, in recent years, have claimed a large part of research activities is that of Integrated or Hybrid Approaches and Methodologies.


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