Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries.Key FeaturesSecond edition of the bestselling book on Machine LearningA practical approach to key frameworks in data science, machine learning, and deep learningUse the most powerful Python libraries to implement machine learning and deep learningGet to know the best practices to improve and optimize you...

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the...

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsKey FeaturesLeverage Python’s most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk – and answer – tough questions of your data with robust statistical models, built for a range of datasets...

Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You’ll learn the steps necessary to create a successful machine-learning appl...

R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anon...

If you’re considering R for statistical computing and data visualization, this book provides a quick and practical guide to just about everything you can do with the open source R language and software environment. You’ll learn how to write R functions and use R packages to help you prepare, visualize, and analyze data. Author Joseph Adler illustrates each process with a wealth of examples from medicine, business, and sports.Updated for R 2.14 and 2.15, this second edition includes...

Structured Parallel Programming offers the simplest way for developers to learn patterns for high-performance parallel programming. Written by parallel computing experts and industry insiders Michael McCool, Arch Robison, and James Reinders, this book explains how to design and implement maintainable and efficient parallel algorithms using a composable, structured, scalable, and machine-independent approach to parallel computing. It presents both theory and practice, and provides detailed ...

Publisher's Note: This edition from 2017 is outdated and not compatible with TensorFlow 2.x or any of the most recent updates to Python libraries. A new edition completely updated and revised for 2020 with seven additional chapters that cover RNNs, AI and big data, fundamental use cases, chatbots, and more, is now available. Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you. Key FeaturesStep int...

Get to grips with the basics of Keras to implement fast and efficient deep-learning modelsKey FeaturesImplement various deep learning algorithms in Keras and see how deep learning can be used in gamesSee how various deep learning models and practical use-cases can be implemented using KerasA practical, hands-on guide with real-world examples to give you a strong foundation in KerasBook DescriptionThis book starts by introduci...

Key FeaturesBook DescriptionThis book targets programmers and scientists who have basic Python knowledge and who are keen to perform scientific and numerical computations with SciPy.What you will learnGet to know the benefits of using the combination of Python, NumPy, SciPy, and matplotlib as a programming environment for scientific purposesCreate and manipulate an object array used by SciPyUse SciPy with large matric...

Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.Authors Adam Gibson and Josh Patterson provide theory on deep learning before introd...

Dive into the future of data science and learn how to build the sophisticated algorithms that are fundamental to deep learning and AI with JavaKey Features\[\*\] Go beyond the theory and put Deep Learning into practice with Java\[\*\]Find out how to build a range of Deep Learning algorithms using a range of leading frameworks including DL4J, Theano and Caffe\[\*\] Whether you’re a data scientist or Java developer, dive in and find out how to ta...