Adobe's new Photoshop 6 is powerful, pixel-based image-editing software. This new book covers the basics and gets readers up and running with Photoshop 6 fast. The authors explain the theory behind the Web--such as color and graphic formats--to help the reader make the best choices. The book is also accompanied by a free Web site with downloadable code and images.
Adobe's new Photoshop 6 is powerful, pixel-based image-editing software. This new book covers the basics and gets readers up and running with Photoshop 6 fast. The authors explain the theory behind the Web--such as color and graphic formats--to help the reader make the best choices. The book is also accompanied by a free Web site with downloadable code and images.
Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations
A guide to building applications with Rails covers such topics as metaprogamming, Active Support library, advanced database functions, security principles, RESTful architecture, and optimizing performance.
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods. This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: · Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
Environmental Systems Engineering and Economics emphasizes the application of optimization, economics, and systems engineering to problems in environmental resources management. This senior level/graduate textbook introduces optimization theory and algorithms that have been successful in resolving water quality and groundwater management problems. Both linear programming and nonlinear optimization are presented. Multiobjective optimization and the linked simulation-optimization (LSO) methodology are also introduced. The basic principles of economics and engineering economics are also discussed to provide a framework for economic decision making. This text contains numerous example problems. Case studies are presented that address water resources management issues in the north China plain, the control of saltwater intrusion in Jakarta, Indonesia, and groundwater resources management in the Yun Lin basin, Taiwan.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.