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Mastering Ceph - Second Edition: Infrastructure storage solutions with the latest Ceph release, 2nd Edition
by Nick FiskIf you are a storage professional, system administrator, and cloud engineers who is looking for solutions to build powerful storage solutions for their cloud and on-premise infrastructure then this book is for you
Kubernetes Design Patterns and Extensions: Enhance your container-cluster management skills and efficiently develop and deploy applications
by Onur YilmazMaster the art of container management with Kubernetes and study robust container orchestration to ensure that your container-based applications sail into production without hiccupsKey FeaturesImplement best practices in cloud-native applications using KubernetesExplore the usage of client libraries and programmatic access to Kubernetes Use your domain expertise to codeBook DescriptionBefore plunging into how Kubernetes works, this book introduces you to the world of container orchestration and describes the recent changes in application development. You'll understand problems that Kubernetes solves and get to grips with using Kubernetes resources to deploy applications. In addition to this, you'll learn to apply the security model of Kubernetes clusters.Kubernetes Design Patterns and Extensions describes how services running in Kubernetes can leverage the platform's security features. Once you've grasped all this, you'll explore how to troubleshoot Kubernetes clusters and debug Kubernetes applications. You also discover how to analyze the networking model and its alternatives in Kubernetes, and apply best practices with design patterns.By the end of this book, you'll have studied all about using the power of Kubernetes for managing your containers.What you will learnUnderstand and classify software designs as per the cloud-native paradigmApply best practices in Kubernetes with design patternsSet up Kubernetes clusters in managed and unmanaged environmentsExplore Kubernetes extension pointsExtend Kubernetes with custom resources and controllers Integrate dynamic admission controllersDevelop and run custom schedulers in KubernetesAnalyze networking models in KubernetesWho this book is forKubernetes Design Patterns and Extensions is for you if you are interested in configuring and troubleshooting Kubernetes clusters and developing microservices-based applications on Kubernetes clusters. DevOps engineers with basic knowledge of Docker will also find this book useful. It is assumed that you are comfortable using command-line tools and programming concepts and languages.
Hands-On Deep Learning for Finance: Implement deep learning techniques and algorithms to create powerful trading strategies
by Luigi Troiano Arjun Bhandari Elena Mejuto VillaTake your quantitative strategies to the next level by exploring nine examples that make use of cutting-edge deep learning technologies, including CNNs, LSTMs, GANs, reinforcement learning, and CapsNets Key Features Implement deep learning techniques and algorithms to build financial models Apply modern AI techniques in quantitative market modeling and investment decision making Leverage Python libraries for rapid development and prototyping Book Description Quantitative methods are the vanguard of the investment management industry. This book shows how to enhance trading strategies and investments in financial markets using deep learning algorithms. This book is an excellent reference to understand how deep learning models can be leveraged to capture insights from financial data. You will implement deep learning models using Python libraries such as TensorFlow and Keras. You will learn various deep learning algorithms to build models for understanding financial market dynamics and exploiting them in a systematic manner. This book takes a pragmatic approach to address various aspects of asset management. The information content in non-structured data like news flow is crystalized using BLSTM. Autoencoders for efficient index replication is discussed in detail. You will use CNN to develop a trading signal with simple technical indicators, and improvements offered by more complex techniques such as CapsNets. Volatility is given due emphasis by demonstrating the superiority of forecasts employing LSTM, and Monte Carlo simulations using GAN for value at risk computations. These are then brought together by implementing deep reinforcement learning for automated trading. This book will serve as a continuing reference for implementing deep learning models to build investment strategies. What you will learn Implement quantitative financial models using the various building blocks of a deep neural network Build, train, and optimize deep networks from scratch Use LSTMs to process data sequences such as time series and news feeds Implement convolutional neural networks (CNNs), CapsNets, and other models to create trading strategies Adapt popular neural networks for pattern recognition in finance using transfer learning Automate investment decisions by using reinforcement learning Discover how a risk model can be constructed using D-GAN Who this book is for If you're a finance or investment professional who wants to lead the development of quantitative strategies, this book is for you. With this practical guide, you'll be able to use deep learning methods for building financial models and incorporating them in your investment process. Anyone who wants to enter the fascinating domain of quantitative finance using the power of deep learning algorithms and techniques will also find this book useful. Basic knowledge of machine learning and Python programming is required.
Practical Ansible 2: Automate infrastructure, manage configuration, and deploy applications with Ansible 2.9
by James Freeman Fabio Alessandro Locati Daniel OhLeverage the power of Ansible to gain complete control over your systems and automate application deployment Key Features Use Ansible 2.9 to automate and control your infrastructure Delve into advanced functionality such as plugins and custom modules in Ansible Automate and orchestrate major cloud platforms such as OpenStack, AWS, and Azure using Ansible Book Description Ansible enables you to automate software provisioning, configuration management, and application roll-outs, and can be used as a deployment and orchestration tool. While Ansible provides simple yet powerful features to automate multi-layer environments using agentless communication, it can also solve other critical IT challenges, such as ensuring continuous integration and continuous deployment (CI/CD) with zero downtime. In this book, you'll work with Ansible 2.9 and learn to solve complex issues quickly with the help of task-oriented scenarios. You'll start by installing and configuring Ansible on Linux and macOS to automate monotonous and repetitive IT tasks and get to grips with concepts such as playbooks, inventories, and network modules. As you progress, you'll gain insight into the YAML syntax and learn how to port between Ansible versions. In addition to this, you'll also understand how Ansible enables you to orchestrate multi-layer environments such as networks, containers, and the cloud. By the end of this Ansible book, you'll be well - versed in writing playbooks and other related Ansible code to overcome just about all of your IT challenges, from infrastructure-as-code provisioning to application deployments, and even handling the mundane day-to-day maintenance tasks that take up so much valuable time. What you will learn Become familiar with the fundamentals of the Ansible framework Set up role-based variables and dependencies Avoid common mistakes and pitfalls when writing automation code in Ansible Extend Ansible by developing your own modules and plugins Contribute to the Ansible project by submitting your own code Follow best practices for working with cloud environment inventories Troubleshoot issues triggered during Ansible playbook runs Who this book is for If you are a DevOps engineer, administrator, or any IT professional looking to automate IT tasks using Ansible, this book is for you. Prior knowledge of Ansible is not necessary.
Mastering Exploratory Analysis with pandas: Build an end-to-end data analysis workflow with Python
by Harish GargExplore Python frameworks like pandas, Jupyter notebooks, and Matplotlib to build data pipelines and data visualizationKey FeaturesLearn to set up data analysis pipelines with pandas and Jupyter notebooksEffective techniques for data selection, manipulation, and visualizationIntroduction to Matplotlib for interactive data visualization using charts and plotsBook DescriptionThe pandas is a Python library that lets you manipulate, transform, and analyze data. It is a popular framework for exploratory data visualization and analyzing datasets and data pipelines based on their properties.This book will be your practical guide to exploring datasets using pandas. You will start by setting up Python, pandas, and Jupyter Notebooks. You will learn how to use Jupyter Notebooks to run Python code. We then show you how to get data into pandas and do some exploratory analysis, before learning how to manipulate and reshape data using pandas methods. You will also learn how to deal with missing data from your datasets, how to draw charts and plots using pandas and Matplotlib, and how to create some effective visualizations for your audience. Finally, you will wrapup your newly gained pandas knowledge by learning how to import data out of pandas into some popular file formats.By the end of this book, you will have a better understanding of exploratory analysis and how to build exploratory data pipelines with Python.What you will learnLearn how to read different kinds of data into pandas DataFrames for data analysis Manipulate, transform, and apply formulas to data imported into pandas DataFramesUse pandas to analyze and visualize different kinds of data to gain real-world insights Extract transformed data form pandas DataFrames and convert it into the formats your application expectsManipulate model time-series data, perform algorithmic trading, derive results on fixed and moving windows, and more Effective data visualization using MatplotlibWho this book is forIf you are a budding data scientist looking to learn the popular pandas library, or a Python developer looking to step into the world of data analysis, this book is the ideal resource you need to get started. Some programming experience in Python will be helpful to get the most out of this course
Hands-On Dashboard Development with Shiny: A practical guide to building effective web applications and dashboards
by Chris BeeleyProgressively explore UI development with Shiny via practical examplesKey FeaturesWrite a Shiny interface in pure HTMLExplore powerful layout functions to make attractive dashboards and other intuitive interfacesGet to grips with Bootstrap and leverage it in your Shiny applicationsBook DescriptionAlthough vanilla Shiny applications look attractive with some layout flexibility, you may still want to have more control over how the interface is laid out to produce a dashboard. Hands-On Dashboard Development with Shiny helps you incorporate this in your applications.The book starts by guiding you in producing an application based on the diamonds dataset included in the ggplot2 package. You’ll create a single application, but the interface will be reskinned and rebuilt throughout using different methods to illustrate their uses and functions using HTML, CSS, and JavaScript. You will also learn to develop an application that creates documents and reports using R Markdown. Furthermore, the book demonstrates the use of HTML templates and the Bootstrap framework. Moving along, you will learn how to produce dashboards using the Shiny command and dashboard package. Finally, you will learn how to lay out applications using a wide range of built-in functions.By the end of the book, you will have an understanding of the principles that underpin layout in Shiny applications, including sections of HTML added to a vanilla Shiny application, HTML interfaces written from scratch, dashboards, navigation bars, and interfaces.What you will learnAdd HTML to a Shiny application and write its interfaces from scratch in HTMLUse built-in Shiny functions to produce attractive and flexible layoutsProduce dashboards, adding icons and notificationsExplore Bootstrap themes to lay out your applicationsGet insights into UI development with hands-on examplesUse R Markdown to create and download reportsWho this book is forIf you have some experience writing Shiny applications and want to use HTML, CSS, and Bootstrap to make custom interfaces, then this book is for you.
Mastering GUI Programming with Python: Develop impressive cross-platform GUI applications with PyQt
by Alan MooreAn advanced guide to creating powerful high-performance GUIs for modern, media-rich applications in various domains such as business and game developmentKey FeaturesGain comprehensive knowledge of Python GUI development using PyQt 5.12Explore advanced topics including multithreaded programming, 3D animation, and SQL databasesBuild cross-platform GUIs for Windows, macOS, Linux, and Raspberry PiBook DescriptionPyQt5 has long been the most powerful and comprehensive GUI framework available for Python, yet there is a lack of cohesive resources available to teach Python programmers how to use it. This book aims to remedy the problem by providing comprehensive coverage of GUI development with PyQt5. You will get started with an introduction to PyQt5, before going on to develop stunning GUIs with modern features. You will then learn how to build forms using QWidgets and learn about important aspects of GUI development such as layouts, size policies, and event-driven programming. Moving ahead, you’ll discover PyQt5’s most powerful features through chapters on audio-visual programming with QtMultimedia, database-driven software with QtSQL, and web browsing with QtWebEngine. Next, in-depth coverage of multithreading and asynchronous programming will help you run tasks asynchronously and build high-concurrency processes with ease. In later chapters, you’ll gain insights into QOpenGLWidget, along with mastering techniques for creating 2D graphics with QPainter. You’ll also explore PyQt on a Raspberry Pi and interface it with remote systems using QtNetwork. Finally, you will learn how to distribute your applications using setuptools and PyInstaller. By the end of this book, you will have the skills you need to develop robust GUI applications using PyQt. What you will learn: Get to grips with the inner workings of PyQt5; Learn how elements in a GUI application communicate with signals and slots; Learn techniques for styling an application; Explore database-driven applications with the QtSQL module; Create 2D graphics with QPainter; Delve into 3D graphics with QOpenGLWidget; Build network and web-aware applications with QtNetwork and QtWebEngine. Who this book is for: This book is for programmers who want to create attractive, functional, and powerful GUIs using the Python language. You’ll also find this book useful if you are a student, professional, or anyone who wants to start exploring GUIs or take your skills to the next level. Although prior knowledge of the Python language is assumed, experience with PyQt, Qt, or GUI programming is not required.
Hands-On Deep Learning for IoT: Train neural network models to develop intelligent IoT applications
by Md. Rezaul Karim Mohammad Abdur Razzaque, PhDImplement popular deep learning techniques to make your IoT applications smarter Key Features Understand how deep learning facilitates fast and accurate analytics in IoT Build intelligent voice and speech recognition apps in TensorFlow and Chainer Analyze IoT data for making automated decisions and efficient predictions Book Description Artificial Intelligence is growing quickly, which is driven by advancements in neural networks(NN) and deep learning (DL). With an increase in investments in smart cities, smart healthcare, and industrial Internet of Things (IoT), commercialization of IoT will soon be at peak in which massive amounts of data generated by IoT devices need to be processed at scale. Hands-On Deep Learning for IoT will provide deeper insights into IoT data, which will start by introducing how DL fits into the context of making IoT applications smarter. It then covers how to build deep architectures using TensorFlow, Keras, and Chainer for IoT. You'll learn how to train convolutional neural networks(CNN) to develop applications for image-based road faults detection and smart garbage separation, followed by implementing voice-initiated smart light control and home access mechanisms powered by recurrent neural networks(RNN). You'll master IoT applications for indoor localization, predictive maintenance, and locating equipment in a large hospital using autoencoders, DeepFi, and LSTM networks. Furthermore, you'll learn IoT application development for healthcare with IoT security enhanced. By the end of this book, you will have sufficient knowledge need to use deep learning efficiently to power your IoT-based applications for smarter decision making. What you will learn Get acquainted with different neural network architectures and their suitability in IoT Understand how deep learning can improve the predictive power in your IoT solutions Capture and process streaming data for predictive maintenance Select optimal frameworks for image recognition and indoor localization Analyze voice data for speech recognition in IoT applications Develop deep learning-based IoT solutions for healthcare Enhance security in your IoT solutions Visualize analyzed data to uncover insights and perform accurate predictions Who this book is for If you're an IoT developer, data scientist, or deep learning enthusiast who wants to apply deep learning techniques to build smart IoT applications, this book is for you. Familiarity with machine learning, a basic understanding of the IoT concepts, and some experience in Python programming will help you get the most out of this book.
R Programming Fundamentals: Deal with data using various modeling techniques
by Kaelen MedeirosStudy data analysis and visualization to successfully analyze data with RKey FeaturesGet to grips with data cleaning methodsExplore statistical concepts and programming in R, including best practicesBuild a data science project with real-world examplesBook DescriptionR Programming Fundamentals, focused on R and the R ecosystem, introduces you to the tools for working with data. To start with, you’ll understand you how to set up R and RStudio, followed by exploring R packages, functions, data structures, control flow, and loops.Once you have grasped the basics, you’ll move on to studying data visualization and graphics. You’ll learn how to build statistical and advanced plots using the powerful ggplot2 library. In addition to this, you’ll discover data management concepts such as factoring, pivoting, aggregating, merging, and dealing with missing values.By the end of this book, you’ll have completed an entire data science project of your own for your portfolio or blog.What you will learnUse basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow controlImport data to R from various formats such as CSV, Excel, and SQLClean data by handling missing values and standardizing fieldsPerform univariate and bivariate analysis using ggplot2Create statistical summary and advanced plots such as histograms, scatter plots, box plots, and interaction plotsApply data management techniques, such as factoring, pivoting, aggregating, merging, and dealing with missing values, on the example datasetsWho this book is forR Programming Fundamentals is for you if you are an analyst who wants to grow in the field of data science and explore the latest tools.
Hands-On Machine Learning with IBM Watson: Leverage IBM Watson to implement machine learning techniques and algorithms using Python
by James MillerThis book is for Data Scientists and Machine Learning Engineers who would like to get introduced to the IBM Cloud and its Machine learning services using practical examples. Basic Python knowledge and limited understanding of Machine Learning will be beneficial.
VBA Automation Cookbook: Solutions to automate routine tasks and increase productivity with Excel and other MS Office applications
by Mike Van NiekerkThis book is for anyone who wants to learn VBA to automate repetitive, tedious, or complex tasks. No prior programming experience is required to get started with this book.
Implementing Azure DevOps Solutions: Learn about Azure DevOps Services to successfully apply DevOps strategies
by Henry Been Maik Van GaagThis book targets software developers and operations specialists interested in implementing DevOps practices for the Azure cloud. Application developers and IT professionals with some experience in software development and development practices would find this book useful. Some familiarity with basic usage of Azure DevOps is an added advantage.
Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features
by Ashish Ranjan Jha Dr. Gopinath PillaiMaster advanced techniques and algorithms for deep learning with PyTorch using real-world examplesKey FeaturesUnderstand how to use PyTorch 1.x to build advanced neural network modelsLearn to perform a wide range of tasks by implementing deep learning algorithms and techniquesGain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much moreBook DescriptionDeep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.What you will learnImplement text and music generating models using PyTorchBuild a deep Q-network (DQN) model in PyTorchExport universal PyTorch models using Open Neural Network Exchange (ONNX)Become well-versed with rapid prototyping using PyTorch with fast.aiPerform neural architecture search effectively using AutoMLEasily interpret machine learning (ML) models written in PyTorch using CaptumDesign ResNets, LSTMs, Transformers, and more using PyTorchFind out how to use PyTorch for distributed training using the torch.distributed APIWho this book is forThis book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.
Microsoft Dynamics NAV Development Quick Start Guide: Get up and running with Microsoft Dynamics NAV
by Alexander DroginLearn development skills and improve productivity when programming in Microsoft Dynamics NAV 2018 - the popular Enterprise Resourse Planning management system used across a variety of industries for business process management Key Features Solve common business problems with the valuable features and flexibility of Dynamics NAV Understand the structure of NAV database - how documents and business entities are mapped to DB tables Design user interface and bind the presentation layer with the data storage Book Description Microsoft Dynamics NAV is an enterprise resource planning (ERP) software suite for organizations. The system offers specialized functionality for manufacturing, distribution, government, retail, and other industries. This book gets you started with its integrated development environment for solving problems by customizing business processes. This book introduces the NAV development environment – C/SIDE. It gives an overview of the internal system language and the most essential development tools. The book will enable the reader to customize and extend NAV functionality with C/AL code, design a user interface through pages, create role centers, and build advanced reports in Microsoft Visual Studio. By the end of the book, you will have learned how to extend the NAV data model, how to write and debug custom code, and how to exchange data with external applications. What you will learn Manage NAV Server configuration with Microsoft Management Console Manage NAV installation with the NAV Administration Shell Create integration events and extend functionality via the NAV event model Run XML Ports from C/AL code Design reports and write client code in RDLC expressions Who this book is for This book is for experienced NAV users who have an understanding of basic programming concepts. Familiarity with NAV development environment or its internal development language-C/AL is not expected.
Azure Serverless Computing Cookbook - Second Edition: Build and monitor Azure applications hosted on serverless architecture using Azure Functions, 2nd Edition
by S Praveen KumarIf you are a cloud administrator, architect, or developer who wants to build scalable systems and deploy serverless applications with Azure functions, then this book is for you. Hands-on experience with core services of Microsoft Azure is required.
Python 3 Object-Oriented Programming: Build robust and maintainable software with object-oriented design patterns in Python 3.8, 3rd Edition
by Dusty PhillipsIf you're new to object-oriented programming techniques, or if you have basic Python skills and wish to learn in depth how and when to correctly apply OOP in Python, this is the book for you. If you are an object-oriented programmer for other languages or seeking a leg up in the new world of Python 3.8, you too will find this book a useful introduction to Python. Previous experience with Python 3 is not necessary.
Getting Started with SketchUp Pro: Embark on your 3D modeling adventure with expert tips, tricks, and best practices
by David S. SellersLevel up your 3D design skills with SketchUp Pro by employing real-world examples and design guidelinesPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesPut your architectural design knowledge to work by exploring practical examplesGain a deeper understanding of SketchUp's GUI, modeling tools, and editing featuresExecute full design creations with detailed steps without getting overwhelmed by SketchUp's advanced functionalitiesBook DescriptionOwing to its ability to create models quickly and with high level of dimensional accuracy, SketchUp Pro has become a popular choice for many industries, including architecture, interior design, landscape architecture, and video game design. If you are seeking to adopt Trimble's exceptional design software, Getting Started with SketchUp Pro serves as an ideal primer to prepare and equip you for its use. This book will help you lay the foundation of a project from scratch, set up appropriate units, and follow a guided path to structure your 3D models. You'll explore the workflows used for creating designs from sketches, making CAD drawings (DWG), and even updating your existing 3D models. Finally, you'll work with extensions and 3D Warehouse to find new workflows and models to add to your skill set. By the end of this SketchUp book, you'll be able to confidently create and share models of your design through CAD drawings and 3D views, and even take them online through the 3D WarehouseWhat you will learnBuild massing 3D models and preliminary designsIdentify optimal methods to boost productivity and efficiency with SketchUp ProExplore SketchUp tools and understand their diverse functionalityGet a complete walkthrough of editing tools, materials, and components in SketchUpCreate and edit components and explore component optionsGet acquainted with SketchUp extensions, 3D Warehouse, and additional tools and resourcesWho this book is forThis book is for architects, landscape architects, interior designers, woodworkers, and 3D modelers with beginner-level knowledge of the architectural design of living spaces. Basic computer skills are all you need to get started.
Mastering Docker Enterprise: A companion guide for agile container adoption
by Mark PanthoferA journey toward containerized applications in production with a cloud-portable, secure, robust and highly available Docker Enterprise platform. Key Features Get an insider's view into the container movement and Docker Enterprise Manage the transformation associated with enterprise container adoption Walk through the enterprise container adoption journey Book Description While known mostly as the open source engine behind tens of millions of server nodes, Docker also offers commercially supported enterprise tooling known as the Docker Enterprise. This platform leverages the deep roots from Docker Engine - Community (formerly Docker CE) and Kubernetes, but adds support and tooling to efficiently operate a secure container platform at scale. With hundreds of enterprises on board, best practices and adoption patterns are emerging rapidly. These learning points can be used to inform adopters and help manage the enterprise transformation associated with enterprise container adoption. This book starts by explaining the case for Docker Enterprise, as well as its structure and reference architecture. From there, we progress through the PoC,pilot and production stages as a working model for adoption, evolving the platform's design and configuration for each stage and using detailed application examples along the way to clarify and demonstrate important concepts.The book concludes with Docker's impact on other emerging software technologies, such as Blockchain and Serverless computing. By the end of this book, you'll have a better understanding of what it takes to get your enterprise up and running with Docker Enterprise and beyond. What you will learn Understand why containers are important to an enterprise Understand the features and components of Docker Enterprise 2 Find out about the PoC, pilot, and production adoption phases Get to know the best practices for installing and operating Docker Enterprise Understand what is important for a Docker Enterprise in production Run Kubernetes on Docker Enterprise Who this book is for This book is for Software Architects, DevOps Engineers, Tech Ops, Docker professionals, or any IT professional working with Docker and containers who wants to move containerized workloads to production. This book discusses the enterprise adoption of Docker and Kubernetes,therefore a basic understanding of Docker concepts will be helpful.
Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis
by Eryk LewinsonSolve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key Features Use powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial data Explore unique recipes for financial data analysis and processing with Python Estimate popular financial models such as CAPM and GARCH using a problem-solution approach Book Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you'll have learned how to effectively analyze financial data using a recipe-based approach. What you will learn Download and preprocess financial data from different sources Backtest the performance of automatic trading strategies in a real-world setting Estimate financial econometrics models in Python and interpret their results Use Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessment Improve the performance of financial models with the latest Python libraries Apply machine learning and deep learning techniques to solve different financial problems Understand the different approaches used to model financial time series data Who this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.
Python Machine Learning By Example - Second Edition: Implement machine learning algorithms and techniques to build intelligent systems, 2nd Edition
by Yuxi (Hayden) LiuThis book is for Machine Learning Aspirants, Data Analysts, Data Engineers who are highly passionate about Machine Learning and wants to start getting employed in Machine Learning assignments. Prior knowledge of python coding is assumed and basic familiarity with the statistical concept is beneficial although not a mandate
OpenCV 4 with Python Blueprints: Build creative computer vision projects with the latest version of OpenCV 4 and Python 3, 2nd Edition
by Michael Beyeler Dr. Menua Gevorgyan Arsen MamikonyanGet to grips with traditional computer vision algorithms and deep learning approaches, and build real-world applications with OpenCV and other machine learning frameworks Key Features Understand how to capture high-quality image data, detect and track objects, and process the actions of animals or humans Implement your learning in different areas of computer vision Explore advanced concepts in OpenCV such as machine learning, artificial neural network, and augmented reality Book Description OpenCV is a native cross-platform C++ library for computer vision, machine learning, and image processing. It is increasingly being adopted in Python for development. This book will get you hands-on with a wide range of intermediate to advanced projects using the latest version of the framework and language, OpenCV 4 and Python 3.8, instead of only covering the core concepts of OpenCV in theoretical lessons. This updated second edition will guide you through working on independent hands-on projects that focus on essential OpenCV concepts such as image processing, object detection, image manipulation, object tracking, and 3D scene reconstruction, in addition to statistical learning and neural networks. You'll begin with concepts such as image filters, Kinect depth sensor, and feature matching. As you advance, you'll not only get hands-on with reconstructing and visualizing a scene in 3D but also learn to track visually salient objects. The book will help you further build on your skills by demonstrating how to recognize traffic signs and emotions on faces. Later, you'll understand how to align images, and detect and track objects using neural networks. By the end of this OpenCV Python book, you'll have gained hands-on experience and become proficient at developing advanced computer vision apps according to specific business needs. What you will learn Generate real-time visual effects using filters and image manipulation techniques such as dodging and burning Recognize hand gestures in real-time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor Learn feature extraction and feature matching to track arbitrary objects of interest Reconstruct a 3D real-world scene using 2D camera motion and camera reprojection techniques Detect faces using a cascade classifier and identify emotions in human faces using multilayer perceptrons Classify, localize, and detect objects with deep neural networks Who this book is for This book is for intermediate-level OpenCV users who are looking to enhance their skills by developing advanced applications. Familiarity with OpenCV concepts and Python libraries, and basic knowledge of the Python programming language are assumed.
Applied Data Visualization with R and ggplot2: Create useful, elaborate, and visually appealing plots
by Dr Tania MoulikDevelop informative and aesthetic visualizations that enable effective data analysis in less timeKey FeaturesDiscover structure of ggplot2, grammar of graphics, and geometric objectsStudy how to design and implement visualization from scratchExplore the advantages of using advanced plotsBook DescriptionApplied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques.You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries.By the end of this book, you’ll have created data visualizations that will impress your clients.What you will learnSet up the R environment, RStudio, and understand structure of ggplot2Distinguish variables and use best practices to visualize themChange visualization defaults to reveal more information about dataImplement the grammar of graphics in ggplot2 such as scales and facetingBuild complex and aesthetic visualizations with ggplot2 analysis methodsLogically and systematically explore complex relationshipsCompare variables in a single visual, with advanced plotting methodsWho this book is forApplied Data Visualization with R and ggplot2 is for you if you are a professional working with data and R. This book is also for students who want to enhance their data analysis skills by adding informative and professional visualizations. It is assumed that you know basics of the R language and its commands and objects.
Odoo 12 Development Cookbook: 190+ unique recipes to build effective enterprise and business applications, 3rd Edition
by Daniel Reis Holger Brunn Alexandre Fayolle Parth GajjarBuild customized solutions for your business using the latest features of Odoo 12 and PythonKey FeaturesGet up to speed with Odoo 12 to create custom and reusable modulesInterconnect your application with other systems by implementing web APIsUnderstand the mechanisms powering the Odoo framework and Odoo.sh to build robust enterprisesBook DescriptionOdoo is a powerful framework known for rapid application development. Its latest release, Odoo 12, introduces tons of new features. With this book, you’ll learn how to develop powerful Odoo applications from scratch, using all the latest features.This Odoo cookbook starts by covering Odoo installation and deployment on the server. Next, you’ll explore the Odoo framework with real-world examples. You’ll create a new Odoo module from the ground up and progress to advanced framework concepts. You’ll also learn how to modify existing applications, including Point of Sale (POS). This book is not just limited to backend development; the advanced JavaScript recipes for creating new views and widgets will help you build beautiful UI elements. As you move forward, you’ll gain insights into website development and become a quality Odoo developer by studying performance optimization, debugging, and automated tests. Finally, you’ll learn the latest concepts like multi-website, In-App Purchasing (IAP), Odoo.sh, and IoT Box.By the end of the book, you’ll have all the knowledge you need to build powerful Odoo applications. The development best practices used in this book will undoubtedly come handy when you are working with the Odoo framework.What you will learnDevelop a module in the Odoo framework and modify the existing flow of any applicationBuild dynamic websites with Odoo CMSCreate and modify backend JavaScript components in Odoo and POSConnect and access any object in Odoo via Remote Procedure Calls (RPC)Illustrate the different tools available in Odoo to implement business processes for your recordsImplement in-app purchase servicesManage, deploy, and test an Odoo instance with the PaaS Odoo.shConfigure IoT Box to add and upgrade Point of Sale (POS) hardwareWho this book is forIf you're a Python developer who wants to develop highly efficient business applications with the latest Odoo framework, or if you just want a solution guide for all your Odoo development issues, this book is for you. Some JavaScript programming and web development experience is necessary to get the most out of this book.
Learn TypeScript 3 by Building Web Applications: Gain a solid understanding of TypeScript, Angular, Vue, React, and NestJS
by Sebastien Dubois Alexis GeorgesLearn TypeScript and many of its features by building state of art web applications from scratch with the help of modern tooling, frameworks, and libraries Key Features Create modern Web applications to help businesses around the world benefit from better quality applications Learn the latest features of TypeScript 3 and use them wisely Explore TDD practices, OOP techniques, and industry best practices to create high-quality and modular apps Book Description TypeScript is a superset of the JavaScript programming language, giving developers a tool to help them write faster, cleaner JavaScript. With the help of its powerful static type system and other powerful tools and techniques it allows developers to write modern JavaScript applications. This book is a practical guide to learn the TypeScript programming language. It covers from the very basics to the more advanced concepts, while explaining many design patterns, techniques, frameworks, libraries and tools along the way. You will also learn a ton about modern web frameworks like Angular, Vue.js and React, and you will build cool web applications using those. This book also covers modern front-end development tooling such as Node.js, npm, yarn, Webpack, Parcel, Jest, and many others. Throughout the book, you will also discover and make use of the most recent additions of the language introduced by TypeScript 3 such as new types enforcing explicit checks, flexible and scalable ways of project structuring, and many more breaking changes. By the end of this book, you will be ready to use TypeScript in your own projects and will also have a concrete view of the current frontend software development landscape. What you will learn Understand and take advantage of TypeScript's powerful Type System Grasp the key concepts and features of Angular, React, Vue.js, and NestJS Handle asynchronous processes using Promises, async/await, Fetch, RxJS, and more Delve into REST, GraphQL and create APIs using Apollo Discover testing concepts, techniques, and tools like TDD, BDD, E2E, Jest Learn Object-Oriented and Functional Programming concepts and leverage those with TypeScript Explore design practices and patterns such as SOLID, MVC, DI and IoC, LoD, AOP, and more Who this book is for This book is for software developers who are willing to discover what TypeScript is and how to leverage it to write great quality software. Developers that are already familiar with TypeScript will find this book useful by learning the languages featured introduced by most recent releases. Basic knowledge of the JavaScript programming is expected.
Hands-On Ensemble Learning with Python: Build highly optimized ensemble machine learning models using scikit-learn and Keras
by George Kyriakides Konstantinos G. MargaritisCombine popular machine learning techniques to create ensemble models using Python Key Features Implement ensemble models using algorithms such as random forests and AdaBoost Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and Keras Book Description Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you'll not only get up to speed on the basic theory but also the application of various ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. Furthermore, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios. What you will learn Implement ensemble methods to generate models with high accuracy Overcome challenges such as bias and variance Explore machine learning algorithms to evaluate model performance Understand how to construct, evaluate, and apply ensemble models Analyze tweets in real time using Twitter's streaming API Use Keras to build an ensemble of neural networks for the MovieLens dataset Who this book is for This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book.