Verleger: 16
Md. Rezaul Karim
Scala is a highly scalable integration of object-oriented nature and functional programming concepts that make it easy to build scalable and complex big data applications. This book is a handy guide for machine learning developers and data scientists who want to develop and train effective machine learning models in Scala.The book starts with an introduction to machine learning, while covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression problems using linear regression, generalized linear regression, logistic regression, support vector machine, and Naïve Bayes algorithms.It also covers tree-based ensemble techniques for solving both classification and regression problems. Moving ahead, it covers unsupervised learning techniques, such as dimensionality reduction, clustering, and recommender systems. Finally, it provides a brief overview of deep learning using a real-life example in Scala.
Kevin Jolly
Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides.This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models.Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions.
Rajdeep Dua, Manpreet Singh Ghotra
This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML.Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML.By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.
Machine Learning with Swift. Artificial Intelligence for iOS
Alexander Sosnovshchenko, Oleksandr Baiev
Machine learning as a field promises to bring increased intelligence to the software by helping us learn and analyse information efficiently and discover certain patterns that humans cannot. This book will be your guide as you embark on an exciting journey in machine learning using the popular Swift language. We’ll start with machine learning basics in the first part of the book to develop a lasting intuition about fundamental machine learning concepts. We explore various supervised and unsupervised statistical learning techniques and how to implement them in Swift, while the third section walks you through deep learning techniques with the help of typical real-world cases. In the last section, we will dive into some hard core topics such as model compression, GPU acceleration and provide some recommendations to avoid common mistakes during machine learning application development. By the end of the book, you'll be able to develop intelligent applications written in Swift that can learn for themselves.
Saif Ahmed, Quan Hua, Shams Ul Azeem
Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you’ll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data ?ow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You’ll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you’ll implement a complete real-life production system from training to serving a deep learning model. As you advance you’ll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you’ll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim.By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment.
Rich Collier, Bahaaldine Azarmi
Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data.As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure.By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.
Rich Collier, Camilla Montonen, Bahaaldine Azarmi
Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection.The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with.By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.
Machupicchu. Między archeologią i polityką
Marta Kania
"Dzięki Machupicchu Peru istnieje. Nasze dziedzictwo i nasza historia trwają nieprzerwanie od wieków" – mówił podczas uroczystości z okazji „100-lecia odkrycia Machupicchu dla Świata” w 2011 roku alcalde Cusco, Luís Flores García. W tle oficjalnych obchodów, w komentarzach prasowych, w rozmowach na ulicach i na plakatach rozwieszanych na murach w centrum miasta padały jednak i gorzkie pytania o to, co właściwie świętuje Peru? Czy jest to fiesta na cześć fałszywego odkrycia Machupicchu? Czy jest to święto ku czci wątpliwych osiągnięć „poszukiwacza przygód” Hirama Binghama? Czy jubileuszowe obchody są faktycznie świętem wszystkich Peruwiańczyków? Czy mieszkańcy Peru mają cieszyć się ze stulecia jawnego łamania prawa narodu peruwiańskiego do własnego dziedzictwa kulturowego? Tematem niniejszej książki są właśnie kontrowersje, które pojawiły się w kontekście jubileuszowego roku „100-lecia odkrycia Machupicchu dla Świata”. Są to rozważania na temat miejsca inkaskiego miasta Machupicchu we współczesnej debacie na temat tożsamości peruwiańskiej, roli, którą zajmuje w regionalnej polityce Departamentu Cusco oraz jego znaczenia w, już nie tylko peruwiańskiej, ale ogólnoświatowej debacie na temat własności dziedzictwa archeologicznego i praw do spuścizny kulturowej. Marta Kania – absolwentka archeologii i kulturoznawstwa (Uniwersytet Jagielloński), doktor nauk humanistycznych Wydziału Historycznego (Uniwersytet Jagielloński). Pracownik Katedry Ameryki Łacińskiej Instytutu Amerykanistyki i Studiów Polonijnych UJ. Od wielu lat prowadzi badania na terenie Ameryki Łacińskiej, przede wszystkim na terenie Peru, gdzie jak w soczewce skupiają się interesujące ją problemy badawcze na temat wielowymiarowych relacji między archeologią a praktyką polityczną, zjawiska mitu i rytuału politycznego oraz społecznych i politycznych aspektów ochrony dziedzictwa kulturowego rdzennej ludności.