Видавець: Packt Publishing
Practical Convolutional Neural Networks. Implement advanced deep learning models using Python
Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari
Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available.Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
Ed Moyle, Diana Kelley
Cybersecurity architects work with others to develop a comprehensive understanding of the business' requirements. They work with stakeholders to plan designs that are implementable, goal-based, and in keeping with the governance strategy of the organization.With this book, you'll explore the fundamentals of cybersecurity architecture: addressing and mitigating risks, designing secure solutions, and communicating with others about security designs. The book outlines strategies that will help you work with execution teams to make your vision a concrete reality, along with covering ways to keep designs relevant over time through ongoing monitoring, maintenance, and continuous improvement. As you progress, you'll also learn about recognized frameworks for building robust designs as well as strategies that you can adopt to create your own designs.By the end of this book, you will have the skills you need to be able to architect solutions with robust security components for your organization, whether they are infrastructure solutions, application solutions, or others.
Diana Kelley, Ed Moyle
Cybersecurity architecture is the discipline of systematically ensuring that an organization is resilient against cybersecurity threats. Cybersecurity architects work in tandem with stakeholders to create a vision for security in the organization and create designs that are implementable, goal-based, and aligned with the organization’s governance strategy.Within this book, you'll learn the fundamentals of cybersecurity architecture as a practical discipline. These fundamentals are evergreen approaches that, once mastered, can be applied and adapted to new and emerging technologies like artificial intelligence and machine learning. You’ll learn how to address and mitigate risks, design secure solutions in a purposeful and repeatable way, communicate with others about security designs, and bring designs to fruition. This new edition outlines strategies to help you work with execution teams to make your vision a reality, along with ways of keeping designs relevant over time. As you progress, you'll also learn about well-known frameworks for building robust designs and strategies that you can adopt to create your own designs.By the end of this book, you’ll have the foundational skills required to build infrastructure, cloud, AI, and application solutions for today and well into the future with robust security components for your organization.
Ed Moyle, Diana Kelley
Cybersecurity architects work with others to develop a comprehensive understanding of the business' requirements. They work with stakeholders to plan designs that are implementable, goal-based, and in keeping with the governance strategy of the organization.With this audiobook, you'll explore the fundamentals of cybersecurity architecture: addressing and mitigating risks, designing secure solutions, and communicating with others about security designs. The book outlines strategies that will help you work with execution teams to make your vision a concrete reality, along with covering ways to keep designs relevant over time through ongoing monitoring, maintenance, and continuous improvement. As you progress, you'll also learn about recognized frameworks for building robust designs as well as strategies that you can adopt to create your own designs.By the end of this audiobook, you will have the skills you need to be able to architect solutions with robust security components for your organization, whether they are infrastructure solutions, application solutions, or others.
Practical Data Analysis Cookbook. Over 60 practical recipes on data exploration and analysis
Tomasz Drabas
Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate, condense and recap, and evaluate data. Its importance has been most visible in the sector of information and communication technologies. It is an employee asset in almost all economy sectors.This book provides a rich set of independent recipes that dive into the world of data analytics and modeling using a variety of approaches, tools, and algorithms. You will learn the basics of data handling and modeling, and will build your skills gradually toward more advanced topics such as simulations, raw text processing, social interactions analysis, and more.First, you will learn some easy-to-follow practical techniques on how to read, write, clean, reformat, explore, and understand your data—arguably the most time-consuming (and the most important) tasks for any data scientist. In the second section, different independent recipes delve into intermediate topics such as classification, clustering, predicting, and more. With the help of these easy-to-follow recipes, you will also learn techniques that can easily be expanded to solve other real-life problems such as building recommendation engines or predictive models. In the third section, you will explore more advanced topics: from the field of graph theory through natural language processing, discrete choice modeling to simulations. You will also get to expand your knowledge on identifying fraud origin with the help of a graph, scrape Internet websites, and classify movies based on their reviews.By the end of this book, you will be able to efficiently use the vast array of tools that the Python environment has to offer.
Hector Cuesta
Plenty of small businesses face big amounts of data but lack the internal skills to support quantitative analysis. Understanding how to harness the power of data analysis using the latest open source technology can lead them to providing better customer service, the visualization of customer needs, or even the ability to obtain fresh insights about the performance of previous products. Practical Data Analysis is a book ideal for home and small business users who want to slice and dice the data they have on hand with minimum hassle.Practical Data Analysis is a hands-on guide to understanding the nature of your data and turn it into insight. It will introduce you to the use of machine learning techniques, social networks analytics, and econometrics to help your clients get insights about the pool of data they have at hand. Performing data preparation and processing over several kinds of data such as text, images, graphs, documents, and time series will also be covered.Practical Data Analysis presents a detailed exploration of the current work in data analysis through self-contained projects. First you will explore the basics of data preparation and transformation through OpenRefine. Then you will get started with exploratory data analysis using the D3js visualization framework. You will also be introduced to some of the machine learning techniques such as, classification, regression, and clusterization through practical projects such as spam classification, predicting gold prices, and finding clusters in your Facebook friends' network. You will learn how to solve problems in text classification, simulation, time series forecast, social media, and MapReduce through detailed projects. Finally you will work with large amounts of Twitter data using MapReduce to perform a sentiment analysis implemented in Python and MongoDB. Practical Data Analysis contains a combination of carefully selected algorithms and data scrubbing that enables you to turn your data into insight.
Practical Data Analysis. Pandas, MongoDB, Apache Spark, and more - Second Edition
Hector Cuesta, Dr. Sampath Kumar
Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service.This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.
Marc Wintjen, Andrew Vlahutin
Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data.After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps.Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries.By the end of this book, you'll have gained the practical skills you need to analyze data with confidence.
Robert Hawker, Nicola Askham
Poor data quality can lead to increased costs, hinder revenue growth, compromise decision-making, and introduce risk into organizations. This leads to employees, customers, and suppliers finding every interaction with the organization frustrating.Practical Data Quality provides a comprehensive view of managing data quality within your organization, covering everything from business cases through to embedding improvements that you make to the organization permanently. Each chapter explains a key element of data quality management, from linking strategy and data together to profiling and designing business rules which reveal bad data. The book outlines a suite of tried-and-tested reports that highlight bad data and allow you to develop a plan to make corrections. Throughout the book, you’ll work with real-world examples and utilize re-usable templates to accelerate your initiatives.By the end of this book, you’ll have gained a clear understanding of every stage of a data quality initiative and be able to drive tangible results for your organization at pace.
Prabhanjan Narayanachar Tattar, Bhushan Purushottam Joshi, Sean...
As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don’t. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python.
Nathan George
Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science.The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion.As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments.By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source.
Allan Visochek
Around 80% of time in data analysis is spent on cleaning and preparing data for analysis. This is, however, an important task, and is a prerequisite to the rest of the data analysis workflow, including visualization, analysis and reporting. Python and R are considered a popular choice of tool for data analysis, and have packages that can be best used to manipulate different kinds of data, as per your requirements. This book will show you the different data wrangling techniques, and how you can leverage the power of Python and R packages to implement them.You’ll start by understanding the data wrangling process and get a solid foundation to work with different types of data. You’ll work with different data structures and acquire and parse data from various locations. You’ll also see how to reshape the layout of data and manipulate, summarize, and join data sets. Finally, we conclude with a quick primer on accessing and processing data from databases, conducting data exploration, and storing and retrieving data quickly using databases.The book includes practical examples on each of these points using simple and real-world data sets to give you an easier understanding. By the end of the book, you’ll have a thorough understanding of all the data wrangling concepts and how to implement them in the best possible way.
Yong Liu, Dr. Matei Zaharia
The book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas.From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You’ll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you’ll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox.By the end of this book, you’ll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.
Miroslav Wengner, Bruno Souza
Design patterns are proven solutions to standard problems in software design and development, allowing you to create reusable, flexible, and maintainable code. This book enables you to upskill by understanding popular patterns to evolve into a proficient software developer.You’ll start by exploring the Java platform to understand and implement design patterns. Then, using various examples, you’ll create different types of vehicles or their parts to enable clarity in design pattern thinking, along with developing new vehicle instances using dedicated design patterns to make the process consistent. As you progress, you’ll find out how to extend vehicle functionalities and keep the code base structure and behavior clean and shiny. Concurrency plays an important role in application design, and you'll learn how to employ a such design patterns with the visualization of thread interaction. The concluding chapters will help you identify and understand anti-pattern utilization in the early stages of development to address refactoring smoothly. The book covers the use of Java 17+ features such as pattern matching, switch cases, and instances of enhancements to enable productivity.By the end of this book, you’ll have gained practical knowledge of design patterns in Java and be able to apply them to address common design problems.
joakim verona
DevOps is a practical field that focuses on delivering business value as efficiently as possible. DevOps encompasses all the flows from code through testing environments to production environments. It stresses the cooperation between different roles, and how they can work together more closely, as the roots of the word imply—Development and Operations.After a quick refresher to DevOps and continuous delivery, we quickly move on to looking at how DevOps affects architecture. You'll create a sample enterprise Java application that you’ll continue to work with through the remaining chapters. Following this, we explore various code storage and build server options. You will then learn how to perform code testing with a few tools and deploy your test successfully. Next, you will learn how to monitor code for any anomalies and make sure it’s running properly. Finally, you will discover how to handle logs and keep track of the issues that affect processes
joakim verona
DevOps is a practical field that focuses on delivering business value as efficiently as possible. DevOps encompasses all code workflows from testing environments to production environments. It stresses cooperation between different roles, and how they can work together more closely, as the roots of the word imply—Development and Operations.Practical DevOps begins with a quick refresher on DevOps and continuous delivery and quickly moves on to show you how DevOps affects software architectures. You'll create a sample enterprise Java application that you’'ll continue to work with through the remaining chapters. Following this, you will explore various code storage and build server options. You will then learn how to test your code with a few tools and deploy your test successfully. In addition to this, you will also see how to monitor code for any anomalies and make sure that it runs as expected. Finally, you will discover how to handle logs and keep track of the issues that affect different processes.By the end of the book, you will be familiar with all the tools needed to deploy, integrate, and deliver efficiently with DevOps.
Richard Boddington
Digital Forensics is a methodology which includes using various tools, techniques, and programming language. This book will get you started with digital forensics and then follow on to preparing investigation plan and preparing toolkit for investigation.In this book you will explore new and promising forensic processes and tools based on ‘disruptive technology’ that offer experienced and budding practitioners the means to regain control of their caseloads. During the course of the book, you will get to know about the technical side of digital forensics and various tools that are needed to perform digital forensics. This book will begin with giving a quick insight into the nature of digital evidence, where it is located and how it can be recovered and forensically examined to assist investigators. This book will take you through a series of chapters that look at the nature and circumstances of digital forensic examinations and explains the processes of evidence recovery and preservation from a range of digital devices, including mobile phones, and other media. This book has a range of case studies and simulations will allow you to apply the knowledge of the theory gained to real-life situations. By the end of this book you will have gained a sound insight into digital forensics and its key components.
Ryan T. White, Archana Tikayat Ray
Discrete mathematics deals with studying countable, distinct elements, and its principles are widely used in building algorithms for computer science and data science. The knowledge of discrete math concepts will help you understand the algorithms, binary, and general mathematics that sit at the core of data-driven tasks.Practical Discrete Mathematics is a comprehensive introduction for those who are new to the mathematics of countable objects. This book will help you get up to speed with using discrete math principles to take your computer science skills to a more advanced level.As you learn the language of discrete mathematics, you’ll also cover methods crucial to studying and describing computer science and machine learning objects and algorithms. The chapters that follow will guide you through how memory and CPUs work. In addition to this, you’ll understand how to analyze data for useful patterns, before finally exploring how to apply math concepts in network routing, web searching, and data science.By the end of this book, you’ll have a deeper understanding of discrete math and its applications in computer science, and be ready to work on real-world algorithm development and machine learning.
Khameel B. Mustapha
SOLIDWORKS is a dominant computer-aided design (CAD) software for the 3D modeling, designing, and analysis of components. This book helps you get to grips with SOLIDWORKS Simulation, which is a remarkable and integral part of SOLIDWORKS predominantly deployed for advanced product performance assessment and virtual prototyping.With this book, you'll take a hands-on approach to learning SOLIDWORKS Simulation with the help of step-by-step guidelines on various aspects of the simulation workflow. You'll begin by learning about the requirements for effective simulation of parts and components, along with the idealization of physical components and their representation with finite element models. As you progress through the book, you'll find exercises at the end of each chapter, and you'll be able to download the geometry models used in all the chapters from GitHub. Finally, you’ll discover how to set up finite element simulations for the static analysis of components under various types of loads, and with different types of materials, from simple isotropic to composite, and different boundary conditions.By the end of this SOLIDWORKS 2022 book, you'll be able to conduct basic and advanced static analyses with SOLIDWORKS Simulation and have practical knowledge of how to best use the family of elements in the SOLIDWORKS Simulation library.
Practical Game AI Programming. Unleash the power of Artificial Intelligence to your game
Micael DaGraça
The book starts with the basics examples of AI for different game genres and directly jumps into defining the probabilities and possibilities of the AI character to determine character movement. Next, you’ll learn how AI characters should behave within the environment created.Moving on, you’ll explore how to work with animations. You’ll also plan and create pruning strategies, and create Theta algorithms to find short and realistic looking game paths. Next, you’ll learn how the AI should behave when there is a lot of characters in the same scene.You'll explore which methods and algorithms, such as possibility maps, Forward Chaining Plan, Rete Algorithm, Pruning Strategies, Wall Distances, and Map Preprocess Implementation should be used on different occasions. You’ll discover how to overcome some limitations, and how to deliver a better experience to the player. By the end of the book, you think differently about AI.
Practical Game Design. A modern and comprehensive guide to video game design - Second Edition
Adam Kramarzewski, Ennio De Nucci
If you’re in search of a cutting-edge actionable guide to game design, your quest ends here! Immerse yourself in the fundamentals of game design with expert guidance from veterans with decades of game design experience across a variety of genres and platforms.The second edition of this book remains dedicated to its original goal of helping you master the fundamentals of game design in a practical manner with the addition of some of the latest trends in game design and a whole lot of fresh, real-world examples from games of the current generation. This update brings a new chapter on games as a service, explaining the evolving role of the game designer and diving deeper into the design of games that are meant to be played forever. From conceptualizing a game idea, you’ll gradually move on to devising a design plan and adapting solutions from existing games, exploring the craft of producing original game mechanics, and eliminating anticipated design risks through testing. You’ll then be introduced to level design, interactive storytelling, user experience and accessibility.By the end of this game design book, you’ll have learned how to wrap up a game ahead of its release date, work through the challenges of designing free-to-play games and games as a service, and significantly improve their quality through iteration, playtesting, and polishing.
Adam Kramarzewski, Ennio De Nucci
If you are looking for an up-to-date and highly applicable guide to game design, then you have come to the right place! Immerse yourself in the fundamentals of game design with this book, written by two highly experienced industry professionals to share their profound insights as well as give valuable advice on creating games across genres and development platforms.This book covers the basics of game design one piece at a time. Starting with learning how to conceptualize a game idea and present it to the development team, you will gradually move on to devising a design plan for the whole project and adapting solutions from other games. You will also discover how to produce original game mechanics without relying on existing reference material, and test and eliminate anticipated design risks. You will then design elements that compose the playtime of a game, followed by making game mechanics, content, and interface accessible to all players. You will also find out how to simultaneously ensure that the gameplay mechanics and content are working as intended.As the book reaches its final chapters, you will learn to wrap up a game ahead of its release date, work through the different challenges of designing free-to-play games, and understand how to significantly improve their quality through iteration, polishing and playtesting.
Valentina Alto
Practical Generative AI with ChatGPT is your hands-on guide to unlocking the full potential of ChatGPT. From building AI assistants and mastering prompt engineering to analyzing documents and images and even generating code, this book equips you with the skills to integrate generative AI into your workflow.Written by a technical architect specializing in AI and intelligent applications, this book provides the tools and knowledge you need to streamline tasks, enhance productivity, and create intelligent solutions. You’ll learn how to craft precise prompts, leverage ChatGPT for daily efficiency, and develop custom AI assistants tailored to your needs.The chapters show you how to use ChatGPT’s multimodal capabilities to generate images with DALL·E and even transform images into code. This ChatGPT book goes beyond basic interactions by showing you how to design custom GPTs and integrate OpenAI’s APIs into your applications. You’ll explore how businesses use OpenAI models, from building AI applications, including semantic search, to creating an AI roadmap. Each chapter is packed with practical examples, ensuring you can apply the techniques right away.By the end of this book, you’ll be well equipped to leverage OpenAI's technology for competitive advantage.
Practical GIS. Learn novice to advanced topics such as QGIS, Spatial data analysis, and more
Gábor Farkas
The most commonly used GIS tools automate tasks that were historically done manually—compiling new maps by overlaying one on top of the other or physically cutting maps into pieces representing specific study areas, changing their projection, and getting meaningful results from the various layers by applying mathematical functions and operations. This book is an easy-to-follow guide to use the most matured open source GIS tools for these tasks.We’ll start by setting up the environment for the tools we use in the book. Then you will learn how to work with QGIS in order to generate useful spatial data. You will get to know the basics of queries, data management, and geoprocessing.After that, you will start to practice your knowledge on real-world examples. We will solve various types of geospatial analyses with various methods. We will start with basic GIS problems by imitating the work of an enthusiastic real estate agent, and continue with more advanced, but typical tasks by solving a decision problem. Finally, you will find out how to publish your data (and results) on the web. We will publish our data with QGIS Server and GeoServer, and create a basic web map with the API of the lightweight Leaflet web mapping library.