Informatyka

Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark

Siamak Amirghodsi, Mohammed Guller, Shuen Mei, Meenakshi...

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks.This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.

Apache Spark Deep Learning Cookbook. Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow

Ahmed Sherif, Amrith Ravindra, Michal Malohlava, Adnan...

Organizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they’re looking to gain faster and more powerful insights from their data. With this book, you’ll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark.Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You’ll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning.By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.

Apache Spark for Data Science Cookbook. Solve real-world analytical problems

Padma Priya Chitturi

Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease. This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.

Apache Spark Machine Learning Blueprints. Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide

Alex Liu

There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data.Packed with a range of project blueprints that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.

Apache Spark Quick Start Guide. Quickly learn the art of writing efficient big data applications with Apache Spark

Shrey Mehrotra, Akash Grade

Apache Spark is a ?exible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases.It will also introduce you to Apache Spark – one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark’s built-in modules for SQL, streaming, machine learning, and graph analysis.Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications.

Apache Tomcat 7 Essentials. This book takes you from beginner to expert in logical stages, covering all the essentials of Tomcat 7 from trouble-free installation to building your own middleware servers. Packed with examples and illustrations

Tanuj Khare

Apache Tomcat (or simply Tomcat) is an open source servlet container developed by the Apache Software Foundation (ASF). The latest major stable release, Apache Tomcat version 7 implements the Servlet 3 and JavaServer Pages 2 specifications from the Java Community Process, and includes many additional features that make it a useful platform for developing and deploying web applications and web services.Apache Tomcat 7 Essentials follows a practical approach to teach installing, configuring, and maintaining Tomcat. It helps you to understand the middle architecture for hosting multiple websites and also provides the confidence to implement middleware support. It imparts to you the capacity to resolve migration issues and also provides regular maintenance solutions. This is the first and only book to cover upgrading to Tomcat 7 from previous versions.The journey of the reader starts at the beginner level and ends at the expert level. The content is designed in such a way that it balances the theory and practical approach for understanding concepts related to handling middle ware and web issues.In this book, you will go through a three-phase life cycle. The first cycle consists of installation, configuration of Tomcat 7 on different OS, and other configurations related to JDBC, port, deployment etc. The second phase deals with the building of enterprise application setup and high availability architecture (clustering load balancing). The third and critical phase will teach you to handle critical issues, performance tuning, and best practices for various environment stacks like dev/QA/stage/production.This book gives you a wider vision of using Tomcat 7 in web technologies and the skill to optimize their performance using Apache Tomcat 7.

Apex Design Patterns. Harness the power of Apex design patterns to build robust and scalable code architectures on the Force.com platform

Anshul Verma, Jitendra Zaa

Apex is an on-demand programming language providing a complete set of features for building business applications – including data models and objects to manage data. Apex being a proprietor programming language from Salesforce to be worked with multi tenant environment is a lot different than traditional OOPs languages like Java and C#. It acts as a workflow engine for managing collaboration of the data between users, a user interface model to handle forms and other interactions, and a SOAP API for programmatic access and integration.Apex Design Patterns gives you an insight to several problematic situations that can arise while developing on Force.com platform and the usage of Design patterns to solve them. Packed with real life examples, it gives you a walkthrough from learning design patterns that Apex can offer us, to implementing the appropriate ones in your own application. Furthermore, we learn about the creational patterns that deal with object creation mechanism and structural patterns that helps to identify the relationship between entities. Also, the behavioural and concurrency patterns are put forward explaining the communication between objects and multi-threaded programming paradigm respectively. We later on, deal with the issues regarding structuring of classes, instantiating or how to give a dynamic behaviour at a runtime, with the help of anti-patterns. We learn the basic OOPs principal in polymorphic and modular way to enhance its capability. Also, best practices of writing Apex code are explained to differentiate between the implementation of appropriate patterns. This book will also explain some unique patterns that could be applied to get around governor limits. By the end of this book, you will be a maestro in developing your applications on Force.com for Salesforce

API Analytics for Product Managers. Understand key API metrics that can help you grow your business

Deepa Goyal

APIs are crucial in the modern market as they allow faster innovation. But have you ever considered your APIs as products for revenue generation?API Analytics for Product Managers takes you through the benefits of efficient researching, strategizing, marketing, and continuously measuring the effectiveness of your APIs to help grow both B2B and B2C SaaS companies. Once you've been introduced to the concept of an API as a product, this fast-paced guide will show you how to establish metrics for activation, retention, engagement, and usage of your API products, as well as metrics to measure the reach and effectiveness of documentation—an often-overlooked aspect of development.Of course, it's not all about the product—as any good product manager knows; you need to understand your customers’ needs, expectations, and satisfaction too. Once you've gathered your data, you’ll need to be able to derive actionable insights from it. This is where the book covers the advanced concepts of leading and lagging metrics, removing bias from the metric-setting process, and bringing metrics together to establish long- and short-term goals.By the end of this book, you'll be perfectly placed to apply product management methodologies to the building and scaling of revenue-generating APIs.