The Algorithmic Foundations of Differential Privacy is meant as a thorough introduction to the problems and techniques of differential privacy, and is an invaluable reference for anyone with an interest in the topic. Found inside – Page iThis open access book explores ways to leverage information technology and machine learning to combat disease and promote health, especially in resource-constrained settings. Found inside – Page iiThis book, fully updated for Python version 3.6+, covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn ... Found insideThis book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Found insideThe book consists of high-quality papers presented at the International Conference on Computational Science and Applications (ICCSA 2019), held at Maharashtra Institute of Technology World Peace University, Pune, India, from 7 to 9 August ... Found insideThis book covers a large number, including the IPython Notebook, pandas, scikit-learn and NLTK. Each chapter of this book introduces you to new algorithms and techniques. Found insideXGBoost is the dominant technique for predictive modeling on regular data. Intended to anyone interested in numerical computing and data science: students, researchers, teachers, engineers, analysts, hobbyists. Found insideExplore machine learning concepts using the latest numerical computing library — TensorFlow — with the help of this comprehensive cookbook About This Book Your quick guide to implementing TensorFlow in your day-to-day machine learning ... Found insideThe main challenge is how to transform data into actionable knowledge. In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. Hawkins develops a powerful theory of how the human brain works, explaining why computers are not intelligent and how, based on this new theory, we can finally build intelligent machines. Found insideDeep learning neural networks have become easy to define and fit, but are still hard to configure. Found inside – Page iWho This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Found insideToday ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Found insideUsing clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, ... In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. Found insideIn this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. Found inside – Page iThis book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Found insideUsing clear explanations, standard Python libraries and step-by-step tutorial lessons you will discover what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling, and how ... Found insideTraditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. Found inside – Page iThis book covers the most popular Python 3 frameworks for both local and distributed (in premise and cloud based) processing. "This book provides a working guide to the C++ Open Source Computer Vision Library (OpenCV) version 3.x and gives a general background on the field of computer vision sufficient to help readers use OpenCV effectively."--Preface. Found inside – Page 1Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Found inside – Page iThis book constitutes the refereed proceedings of the 17th International Conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2016, held in Varna, Bulgaria in September 2015. Found insideThis practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape ... For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Found inside – Page 1About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Found insideThis book is about making machine learning models and their decisions interpretable. Found insideThis book is your entry point to machine learning. This book starts with an introduction to machine learning and the Python language and shows you how to complete the setup. Found insideThis book constitutes the thoroughly refereed post-conference proceedings of the 10th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2018, held in conjunction with the 44th International Conference on Very Large ... Found insideWith this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas ... Found insideThe book also discusses Google Colab, which makes it possible to write Python code in the cloud. Found insideEach chapter consists of several recipes needed to complete a single project, such as training a music recommending system. Author Douwe Osinga also provides a chapter with half a dozen techniques to help you if you’re stuck. Found insideThis book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels. After reading this book, you’ll have the solid foundation you need to start a career in data science. Found insideThis book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems. Found insideThe Python ecosystem with scikit-learn and pandas is required for operational machine learning. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. In this book, we give a fairly comprehensive presentation of MDS. For the reader with applied interests only, the first six chapters of Part I should be sufficient. The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is ... Found insideDeep learning is the most interesting and powerful machine learning technique right now. Top deep learning libraries are available on the Python ecosystem like Theano and TensorFlow. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. Found inside – Page 332... https://stackoverflow.com/questions/ 21638130/tutorial-for-scipy-cluster-hierarchy PCA example with Iris Data-set, http://scikit-learn.org/stable/auto_ ... Found insideThis two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September ... Found inside – Page 1The Complete Beginner’s Guide to Understanding and Building Machine Learning Systems with Python Machine Learning with Python for Everyone will help you master the processes, patterns, and strategies you need to build effective learning ... Found insideLinear algebra is a pillar of machine learning. This is the definitive edition of the hugely popular collection of Einstein quotations that has sold tens of thousands of copies worldwide and been translated into twenty-five languages. Found inside – Page iThis book is a good starting point for people who want to get started in deep learning for NLP. Found insideBuild your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. 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