Python machine learning

本書是《Deep Learning : 用Python進行深度學習的基礎理論實作》的續篇，將延續上一本書，繼續介紹與深度學習有關的技術。本書尤其偏重在自然語言處理及時間序列資料處理上，使用深度學習，挑戰各式各樣的問題。和上一本著作一樣，以「從零開始建構」為概念，詳盡介紹與深度學習有關的先進技術。 簡單來說，自然語言處理是指，讓電腦瞭解我們平常說話內容的技術。事實上，這種自然語言處理技術已經大大改變

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

Large-scale data analytics using machine learning (ML) underpins many modern data-driven applications. ML systems provide means of specifying and executing these ML workloads in an efficient and scala

This book constitutes the post-conference proceedings of the 4th International Conference on Machine Learning, Optimization, and Data Science, LOD 2018, held in Volterra, Italy, in September 2018.The

Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investmentBig Data and Machine Learning in Quantitative Investment is not just about demonstrat

Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-wor

Create AI applications in Python and lay the foundations for your career in data scienceKey FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail wit

A metaheuristic is a higher-level procedure designed to select a heuristic (partial search algorithm) that may lead to a sufficiently good solution to an optimization problem, especially with incomple

Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learnKey FeaturesDevelop a range of healthcare analytics projects using real-world datasetsImplement key machin

This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and source codes. It can be used as a textbook in graduation or und

This book discusses some of the innumerable ways in which computational methods can be used to facilitate research in biology and medicine - from storing enormous amounts of biological data to solving

This book explores some of the emerging scientific and technological areas in which the need for data analytics arises and is likely to play a significant role in the years to come. At the dawn of the

This book provides frequently studied and used machines together with soft computing methods such as evolutionary computation. The main topics of the machine learning cover Artificial Neural Networks

"This book provides the information on the machine learning techniques that can be in business problems in efficient way. It encourages the data Scientists to take a proactive attitude toward applying

The book covers tools in the study of online social networks such as machine learning techniques, clustering, and deep learning. A variety of theoretical aspects, application domains, and case studies

Machine learning is an interdisciplinary field of science and engineering that studies mathematical theories and practical applications of systems that learn. This book introduces theories, methods and applications of density ratio estimation, which is a newly emerging paradigm in the machine learning community. Various machine learning problems such as non-stationarity adaptation, outlier detection, dimensionality reduction, independent component analysis, clustering, classification and conditional density estimation can be systematically solved via the estimation of probability density ratios. The authors offer a comprehensive introduction of various density ratio estimators including methods via density estimation, moment matching, probabilistic classification, density fitting and density ratio fitting, as well as describing how these can be applied to machine learning. The book provides mathematical theories for density ratio estimation including parametric and non-parametric conve

Edited by two of the leading experts in the field, this book provides a comprehensive reference book on feature engineering. The book will provide a description of problems/applications/dataset types

This Book discusses machine learning for model order reduction, which can be used in modern VLSI design to predict the behavior of an electronic circuit, via mathematical models that predict behavior.

This book combines the advantages of high-dimensional data visualization and machine learning in the context of identifying complex n-D data patterns. It vastly expands the class of reversible lossles

This book focuses on the study of possible adaptive sampling mechanisms for haptic data compression aimed at applications like tele-operations and tele-surgery. Demonstrating that the selection of the

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a t

A new collection of stories, including some that have never before been seen, from the New York Times best-selling author of the Silo trilogy Hugh Howey is known for crafting riveting and immersive pa

A new collection of stories, including some that have never before been seen, from the New York Times best-selling author of the Silo trilogy Hugh Howey is known for crafting riveting and immersive pa

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolu

Large-scale machine learning, currently focused on the internet and/or social network analysis, could prove highly beneficial in the study of earth science, a broad multidisciplinary field of study th

This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based t

This book provides the first systematic study of facial kinship verification, a new research topic in biometrics. It presents three key aspects of facial kinship verification: 1) feature learning for

Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-pr

This authoritative, expanded and updated second edition of Encyclopedia of Machine Learning and Data Mining provides easy access to core information for those seeking entry into any aspect within the

Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationa

The goal of this introductory graduate textbook is to cover the statistical and computational methods needed by molecular biologists in order to analyze their own data without the help of a bioinforma