No pleasure lasts long unless there is variety in it. Publilius Syrus, Moral Sayings We´ve been very fortunate to receive fantastic feedback from our readers during the last four years, since the first edition of How to Solve It: Modern Heuristics was published in 1999. It´s heartening to know that so many people appreciated the book and, even more importantly, were using the book to help them solve their problems. One professor, who published a review of the book, said that his students had given the best course reviews he´d seen in 15 years when using our text. There can be hardly any better praise, except to add that one of the book reviews published in a SIAM journal received the best review award as well. We greatly appreciate your kind words and personal comments that you sent, including the few cases where you found some typographical or other errors. Thank you all for this wonderful support.
Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.
Learn how to solve challenging machine learning problems with TensorFlow, Google´s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals.
We´re losing hundreds of billions of dollars a year on broken software, and great new ideas such as agile development and Scrum don´t always pay off. But there´s hope. The nine software development practices in Beyond Legacy Code are designed to solve the problems facing our industry.
Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You´ll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you´ll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building Discover common neural network frameworks with Amazon SageMaker Solve computer vision problems with Amazon Rekognition Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem.
Fun introduction to game development by well-known game designer using PuzzleScript, a free online tool for creating puzzles/platform games. PuzzleScript is a free, web-based tool you can use to create puzzle games. In a PuzzleScript game, you move objects around to solve problems and play through the levels. In Make Your Own PuzzleScript Games! you´ll learn how to use PuzzleScript to create interactive games--no programming experience necessary! Learn the basics like how to make objects, create rules, and add levels. You´ll also learn how to edit, test, and share your games online. Learn how to: ? Decorate your game with fun backgrounds ? Write rules that define how objects interact ? Add obstacles like laser guns and guards ? Herd cats and even pull off a robot heist! With colorful illustrations and plenty of examples for inspiration, Make Your Own PuzzleScript Games! will take you from puzzle solver to game designer in just a few clicks!
The next generation of problems will not have deterministic solutions - the solutions will be statistical that rely on mountains, or mounds, of data. Bayesian methods offer a very flexible and extendible framework to solve these types of problems. For programming students with minimal background in mathematics, this example-heavy guide emphasizes the new technologies that have allowed the inference to be abstracted from complicated underlying mathematics. Using Bayesian Methods for Hackers, students can start leveraging powerful Bayesian tools right now -- gradually deepening their theoretical knowledge while already achieving powerful results in areas ranging from marketing to finance.
The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject