Programming and scripting languages: general

This book provides an introduction to R programming and a summary of financial mathematics.

Development in methodology on longitudinal data is fast.

If you know a little bit about financial mathematics but don't yet know a lot about programming, then C++ for Financial Mathematics is for you.

If you know a little bit about financial mathematics but don't yet know a lot about programming, then C++ for Financial Mathematics is for you.

The predominant language used in embedded microprocessors, assembly language lets you write programs that are typically faster and more compact than programs written in a high-level language and provide greater control over the program applications.

Optics is an enabling science that forms a basis for our technological civilization.

Teach Your Students How to Use Computing to Explore Powerful and Creative IdeasIn the twenty-first century, computers have become indispensable in music making, distribution, performance, and consumption.

Learn how to write R code with fewer bugs.

After the fundamental volume and the advanced technique volume, this volume focuses on R applications in the quantitative investment area.

This book discusses advanced topics such as R core programing, object oriented R programing, parallel computing with R, and spatial data types.

Unlike other books about R, written from the perspective of statistics, this book is written from the perspective of programmers, providing a channel for programmers with expertise in other programming languages to quickly understand R.

This text is a manual for undergraduate students in engineering and the natural sciences to discover how computer programming works.

Praise for the first edition:"e;The well-written, comprehensive book.

Praise for the first edition:"e;The well-written, comprehensive book.

Computer simulation is an effective and popular universal tool that can be applied to almost all disciplines.

Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter classes including the median filter and a number of its extensions (e.

Exploring the Infinite addresses the trend toward a combined transition course and introduction to analysis course.

Exploring the Infinite addresses the trend toward a combined transition course and introduction to analysis course.

Apply machine and deep learning to solve some of the challenges in the oil and gas industry.

Build the foundational data science skills necessary to work with and better understand complex data science algorithms.

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark using Python.

Think from a framework design perspective and move beyond straightforward coding skills.

Explore the designs of the Spring MVC and WebFlux frameworks, and apply similar designs and techniques to your own code.

Transition from a back-end developer to a full-stack developer with knowledge of all the dimensions of web application development, namely, front-end, back-end and server-side software.

Leverage Apache Spark within a modern data engineering ecosystem.

Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure.

Write your first code in Java using simple, step-by-step examples that model real-word objects and events, making learning easy.

This book offers comprehensive coverage of the various technologies and techniques used to build technical products.

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python.

Discover the functioning and example uses of the Common Lisp condition system.

Build great voice apps of any complexity for any domain by learning both the how's and why's of voice development.

Design and develop end-to-end, production-grade computer vision projects for real-world industry problems.

Discover key information buried in the noise of data by learning a variety of anomaly detection techniques and using the Python programming language to build a robust service for anomaly detection against a variety of data types.

Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis.

Harness the power of Quarkus, the supersonic subatomic cloud-native Java platform from Red Hat.

Gain a thorough knowledge of Lucene's capabilities and use it to develop your own search applications.

Expand your knowledge of Java with this entertaining learning guide, which features 100+ exercises and programming challenges.

Explore the fundamentals of data analysis, and statistics with case studies using Python.

Write solid, secure, object-oriented code in the new PHP 8.

Apply computer vision and machine learning concepts in developing business and industrial applications ?

Learn the fundamentals of containerization and get acquainted with Docker.

Get hands-on knowledge of how BERT (Bidirectional Encoder Representations from Transformers) can be used to develop question answering (QA) systems by using natural language processing (NLP) and deep learning.

Apply a methodology and practical solutions for monitoring the behavior of the Internet of Things (IoT), industrial control systems (ICS), and other critical network devices with the inexpensive Raspberry Pi.

Discover the capabilities of PySpark and its application in the realm of data science.

Use Traefik as a load balancer or a reverse proxy for microservices-based architecture.

Learn how to harness modern deep-learning methods in many contexts.

Leverage machine and deep learning models to build applications on real-time data using PySpark.

Graduate to the next level of your software development career, learning the tools you need to successfully manage the complexity of modern software systems.

Build enterprise-grade microservices in the SAP HANA Advanced Model (XSA).

Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference.