Computer Graphics Programming in OpenGL With C++ (Edition 3). Mastering 3D Graphics and Animation Techniques

Mercury Learning and Information, V. Scott Gordon, John L. Clevenger

This edition provides step-by-step instruction on modern OpenGL 4.0+ GLSL shader programming with C++, covering theoretical foundations of 3D computer graphics. Every shader stage is explored, from basics like modeling, textures, lighting, and shadows, to advanced techniques such as tessellation, noise maps, water, and stereoscopy. Expanded coverage includes camera control, refraction, and ray tracing with bounding volume hierarchies. Companion files include source code, shaders, model files, and skyboxes for all examples in the book.Understanding these concepts is crucial for creating sophisticated 3D graphics applications. The course begins with OpenGL basics and pipeline understanding, followed by mathematical foundations and managing 3D graphics data. It covers texture mapping, 3D models, lighting, shadows, enhancing surface detail, and advanced topics like parametric surfaces, tessellation, and geometry shaders. Final chapters delve into simulating water, ray tracing, and stereoscopy for 3D graphics.This book equips readers with the knowledge to master OpenGL programming and 3D graphics, making it invaluable for developers and enthusiasts aiming to advance their skills. The practical approach and comprehensive examples ensure thorough learning, bridging the gap between theory and real-world application.

Mathematics for Business. Explore Essential Mathematical Concepts and Techniques for Decision Making

Mercury Learning and Information, Gary Bronson, Richard Bronson, Maureen Kieff

This course provides a comprehensive understanding of quantitative methods essential for economic forecasting, resource allocation, portfolio analysis, inventory management, data-mining, and addressing social and climate challenges. Starting with foundational topics like finite mathematics and the mathematics of finance, it progresses to differential calculus, optimization, and curve fitting. These concepts are vital for solving contemporary business problems.Learners will explore algebra, finite math, finance mathematics, calculus, optimization techniques, and curve fitting, applying these methods to realistic business scenarios. Topics include cash flow, amortization, interest, loans, annuities, revenue/cost models, break-even analysis, inventory control, and econometrics. Each section includes extensive examples and exercises, reinforced by key terms and concepts, making the material accessible and practical.The course begins with basic mathematical concepts and advances through increasingly complex topics. By the end, learners will have the tools to tackle various business problems using quantitative methods, making this course invaluable for anyone in the business field. This structured approach ensures both theoretical knowledge and practical application, preparing learners for real-world challenges.

Natural Language Processing Fundamentals for Developers. A Practical Guide to Building NLP Applications

Mercury Learning and Information, Oswald Campesato

This book is for developers seeking an overview of basic concepts in Natural Language Processing (NLP). It caters to those with varied technical backgrounds, offering numerous code samples and listings to illustrate the wide range of topics covered. The journey begins with managing data relevant to NLP, followed by two chapters on fundamental NLP concepts. This foundation is reinforced with Python code samples that bring these concepts to life.The book then delves into practical NLP applications, such as sentiment analysis, recommender systems, COVID-19 analysis, spam detection, and chatbots. These examples provide real-world context and demonstrate how NLP techniques can be applied to solve common problems. The final chapter introduces advanced topics, including the Transformer architecture, BERT-based models, and the GPT family, highlighting the latest state-of-the-art developments in the field.Appendices offer additional resources, including Python code samples on regular expressions and probability/statistical concepts, ensuring a well-rounded understanding. Companion files with source code and figures enhance the learning experience, making this book a comprehensive guide for mastering NLP techniques and applications.

Discrete Mathematics With Cryptographic Applications. A Self-Teaching Guide to Unlocking the Power of Advanced Concepts and Computational Techniques

Mercury Learning and Information, Alexander I. Kheyfits

This book offers a comprehensive guide to discrete mathematics and its applications to cryptography. It is designed for students and professionals in fields such as discrete mathematics and finite mathematics, with all necessary prerequisites clearly explained and illustrated. The text introduces key concepts in number theory, coding theory, and information theory, which are essential for understanding cryptography.Understanding discrete mathematics is crucial for anyone working in cryptography and related fields. The book begins with a survey of elementary functions and moves on to propositional algebra, set theory, and algebraic structures like groups, rings, and fields. It covers binary relations, combinatorics, and elements of number theory, which are fundamental to cryptographic methods.Readers will explore topics such as Boolean functions, hashing functions, cryptographic maps, combinatorial circuits, and graph theory. The book also delves into advanced areas like finite automata, game theory, and Turing machines. Through numerous examples, problems, and solutions, readers will gain a solid foundation in discrete mathematics and its cryptographic applications.

Data Literacy With Python. A Comprehensive Guide to Understanding and Analyzing Data with Python

Mercury Learning and Information, Oswald Campesato

This book ushers readers into the world of data, emphasizing its importance in modern industries and how its management leads to insightful decision-making. Using Python 3, the book introduces foundational data tasks and progresses to advanced model training concepts. Detailed, step-by-step Python examples help readers master training models, starting with the kNN algorithm and moving to other classifiers with minimal code adjustments. Tools like Sweetviz, Skimpy, Matplotlib, and Seaborn are introduced for hands-on chart and graph rendering.The course begins with working with data, detecting outliers and anomalies, and cleaning datasets. It then introduces statistics and progresses to using Matplotlib and Seaborn for data visualization. Each chapter builds on the previous one, ensuring a comprehensive understanding of data management and analysis.These concepts are crucial for making data-driven decisions. This book transitions readers from basic data handling to advanced model training, blending theoretical knowledge with practical skills. Companion files with source code and data sets enhance the learning experience, making this book an invaluable resource for mastering data science with Python.

Bash for Data Scientists. A Comprehensive Guide to Shell Scripting for Data Science Tasks

Mercury Learning and Information, Oswald Campesato

This book introduces powerful command line utilities for creating efficient shell scripts to process datasets. Using the bash shell, the examples and scripts focus on small datasets to help readers understand the features of grep, sed, and awk. Companion files with code are available for download from the publisher.The course starts with an introduction to the basics, covering files and directories, and useful commands. It then progresses to conditional logic and loops, providing a solid foundation for processing datasets. Detailed chapters on using grep, sed, and awk illustrate their capabilities in handling and cleaning various types of datasets effectively.Advanced topics include processing datasets with Pandas, exploring NoSQL, SQLite, and Python. The book equips data scientists, analysts, and anyone seeking shell-based solutions with practical skills. By the end, users will be adept at creating robust scripts for dataset processing, combining command line utilities for optimal results.

Tensor Analysis for Engineers. Mastering Coordinate Systems, Transformations and Applications using Mathematics

Mercury Learning and Information, Mehrzad Tabatabaian

This book is a comprehensive guide to tensor analysis for engineers and applied scientists, focusing on practical problem-solving in Euclidean space. It covers Cartesian coordinate systems and curvilinear coordinates like cylindrical and spherical, with examples and calculations. Expanded content includes rigid body rotation, Cartesian tensors, Euler angles, and quaternion methods.The course begins with coordinate systems, moving through curvilinear systems, basis vectors, and scale factors. It covers contravariant and physical components, tensor transformations, and mixed and metric tensors. Advanced topics include gradient operators, derivative forms, Cartesian tensor transformations, and coordinate-independent equations. The book features relations for selected coordinate systems, rigid body rotation methods, and numerous worked-out examples and exercises.Mastering tensor analysis is crucial for complex engineering and science problems. This book transitions from basic concepts to advanced applications, blending theory with practical examples. Clear explanations, figures, and exercises enhance learning, making this an essential resource for tensor analysis in Euclidean space.

Artificial Intelligence, Machine Learning, and Deep Learning. A Practical Guide to Advanced AI Techniques

Mercury Learning and Information, Oswald Campesato

This book introduces AI, then explores machine learning, deep learning, natural language processing (NLP), and reinforcement learning. Readers learn about classifiers like logistic regression, k-NN, decision trees, random forests, and SVMs. It delves into deep learning architectures such as CNNs, RNNs, LSTMs, and autoencoders, with Keras-based code samples supplementing the theory.Starting with a foundational AI overview, the course progresses into machine learning, explaining classifiers and their applications. It continues with deep learning, focusing on architectures like CNNs and RNNs. Advanced topics include LSTMs and autoencoders, essential for modern AI. The book also covers NLP and reinforcement learning, emphasizing their importance.Understanding these concepts is vital for developing advanced AI systems. This book transitions you from beginner to proficient AI practitioner, combining theoretical knowledge and practical skills. Appendices on Keras, TensorFlow 2, and Pandas enrich the learning experience. By the end, readers will understand AI principles and be ready to apply them in real-world scenarios.