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Pivotal Certified Professional Spring Developer Exam
Pivotal Certified Professional Spring Developer Exam

Pass the Pivotal Certified Professional exam using source code examples, study summaries, and mock exams. In this book, you'll find a descriptive overview of certification-related Spring modules and a single example application demonstrating the use of all required Spring modules. Also, it is suitable as an introductory primer for Spring newcomers. Furthermore, in Pivotal Certified Professional Spring Developer Exam: A Study Guide each chapter contains a brief study summary and question set, and the book's free downloadable source code package includes one mock exam (50 questions - like a real exam). After using this study guide, you will be ready to take and pass the Pivotal Certified Professional exam. When you become Pivotal Certified, you will have one of the most valuable credentials in Java. The demand for Spring skills is skyrocketing. Pivotal certification helps you advance your skills and your career, and get the maximum benefit from Spring. Passing the exam demonstrates ...
Mastering Machine Learning with Python in Six Steps
Mastering Machine Learning with Python in Six Steps

Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book's approach is based on the "Six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You'll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining / analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you'll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. All the code presented in the book will be available in the form of iPython ...
Foundations for Analytics with Python
Foundations for Analytics with Python

If you're like many of Excel's 750 million users, you want to do more with your data - like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats - by using Python. After author Clinton Brownley takes you through Python basics, you'll be able to write simple scripts for processing data in spreadsheets as well as databases. You'll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary.Create and run your own Python scripts by learning basic syntax;Use Python's csv module to read and parse CSV files;Read multiple Excel worksheets and workbooks with the xlrd module;Perform database operations in MySQL or with the mysqlclient module;Create Python applications to find spe ...
Mastering Azure Analytics
Mastering Azure Analytics

Microsoft Azure has over 20 platform-as-a-service (PaaS) offerings that can act in support of a big data analytics solution. So which one is right for your project? This practical book helps you understand the breadth of Azure services by organizing them into a reference framework you can use when crafting your own big data analytics solution. You'll not only be able to determine which service best fits the job, but also learn how to implement a complete solution that scales, provides human fault tolerance, and supports future needs.Understand the fundamental patterns of the data lake and lambda architecture;Recognize the canonical steps in the analytics data pipeline and learn how to use Azure Data Factory to orchestrate them;Implement data lakes and lambda architectures, using Azure Data Lake Store, Data Lake Analytics, HDInsight (including Spark), Stream Analytics, SQL Data Warehouse, and Event Hubs;Understand where Azure Machine Learning fits i ...
The Cucumber Book, 2nd Edition
The Cucumber Book, 2nd Edition

Express your customers wild ideas as a set of clear, executable specifications that everyone on the team can read. Feed those examples into Cucumber and let it guide your development. Build just the right code to keep your customers happy. You can use Cucumber to test almost any system or any platform. Get started by using the core features of Cucumber and working with Cucumber's Gherkin DSL to describe - in plain language - the behavior your customers want from the system. Then write Ruby code that interprets those plain-language specifications and checks them against your application. Next, consolidate the knowledge you've gained with a worked example, where you'll learn more advanced Cucumber techniques, test asynchronous systems, and test systems that use a database. Recipes highlight some of the most difficult and commonly seen situations the authors have helped teams solve. With these patterns and techniques, test Ajax-heavy web applications with Capybara and Selenium, REST we ...
Scalable Cloud Ops with Fugue
Scalable Cloud Ops with Fugue

Cloud computing represents the greatest shift in computing in more than a decade. But the promise of the cloud is unrealized. The cloud isn't just about shedding the physical data center - it's about shedding the data center mindset. Cloud infrastructure can be controlled via API calls. This means we can view it as a giant general-purpose computer - and program it. That's where the Fugue operating system and the Ludwig language come in. They automate the creation, operation, enforcement, and termination of infrastructure in the cloud. This definitive Fugue guide starts with a simple website and moves on to a more robust application with evolving infrastructure needs as you walk through the steps to harnessing the cloud. With Amazon Web Services, launch infrastructure quickly. Debug in design time. Automate deployment and enforcement of your cloud. Centralize your change control process and automate continuous auditing. Rest easy knowing configuration drift, unwanted changes, and inf ...
Stream Processing with Apache Flink
Stream Processing with Apache Flink

Get started with Apache Flink, the open source framework that enables you to process streaming data - such as user interactions, sensor data, and machine logs - as it arrives. With this practical guide, you'll learn how to use Apache Flink's stream processing APIs to implement, continuously run, and maintain real-world applications. Authors Fabian Hueske, one of Flink's creators, and Vasia Kalavri, a core contributor to Flink's graph processing API (Gelly), explains the fundamental concepts of parallel stream processing and shows you how streaming analytics differs from traditional batch data analysis. Software engineers, data engineers, and system administrators will learn the basics of Flink's DataStream API, including the structure and components of a common Flink streaming application.Solve real-world problems with Apache Flink's DataStream API;Set up an environment for developing stream processing applications for Flink;Design streaming applications an ...
Natural Language Processing with PyTorch
Natural Language Processing with PyTorch

Natural Language Processing (NLP) offers unbounded opportunities for solving interesting problems in artificial intelligence, making it the latest frontier for developing intelligent, deep learning-based applications. If you're a developer or researcher ready to dive deeper into this rapidly growing area of artificial intelligence, this practical book shows you how to use the PyTorch deep learning framework to implement recently discovered NLP techniques. To get started, all you need is a machine learning background and experience programming with Python. Authors Delip Rao and Goku Mohandas provide you with a solid grounding in PyTorch, and deep learning algorithms, for building applications involving semantic representation of text. Each chapter includes several code examples and illustrations.Get extensive introductions to NLP, deep learning, and PyTorch;Understand traditional NLP methods, including NLTK, SpaCy, and gensim;Explore embeddings: high quality ...
Gray Hat C#
Gray Hat C#

Learn to use C#'s powerful set of core ­libraries to automate tedious yet important tasks like fuzzing, performing vulnerability scans, and analyzing malware. With some help from Mono, you'll write your own practical security tools that will run on Windows, OS X, Linux, and even mobile devices. After a crash course in C# and some of its advanced features, you'll learn how to: Generate shellcode in Metasploit to create cross-platform and cross-architecture payloads; Automate Nessus, OpenVAS, and sqlmap to scan for vulnerabilities and exploit SQL injections; Write a .NET decompiler for OS X and Linux; Parse and read offline registry hives to dump system information; Automate the security tools Arachni and Metasploit using their MSGPACK RPCs. Streamline and simplify your workday by making the most of C#'s extensive repertoire of powerful tools and libraries with Gray Hat C#. ...
Machine Learning and Security
Machine Learning and Security

Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself. With this practical guide, you'll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters; Quickly detect anomalies, including breaches, fraud, and impending system failure; Conduct malware analysis by extracting useful information from computer binaries; Uncover at ...
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