Windows Forensics Cookbook. Over 60 practical recipes to acquire memory data and analyze systems with the latest Windows forensic tools

Scar de Courcier, Oleg Skulkin

Windows Forensics Cookbook provides recipes to overcome forensic challenges and helps you carry out effective investigations easily on a Windows platform. You will begin with a refresher on digital forensics and evidence acquisition, which will help you to understand the challenges faced while acquiring evidence from Windows systems. Next you will learn to acquire Windows memory data and analyze Windows systems with modern forensic tools. We also cover some more in-depth elements of forensic analysis, such as how to analyze data from Windows system artifacts, parse data from the most commonly-used web browsers and email services, and effectively report on digital forensic investigations. You will see how Windows 10 is different from previous versions and how you can overcome the specific challenges it brings. Finally, you will learn to troubleshoot issues that arise while performing digital forensic investigations. By the end of the book, you will be able to carry out forensics investigations efficiently.

Mastering Distributed Tracing. Analyzing performance in microservices and complex systems

Yuri Shkuro

Mastering Distributed Tracing will equip you to operate and enhance your own tracing infrastructure. Through practical exercises and code examples, you will learn how end-to-end tracing can be used as a powerful application performance management and comprehension tool. The rise of Internet-scale companies, like Google and Amazon, ushered in a new era of distributed systems operating on thousands of nodes across multiple data centers. Microservices increased that complexity, often exponentially. It is harder to debug these systems, track down failures, detect bottlenecks, or even simply understand what is going on. Distributed tracing focuses on solving these problems for complex distributed systems. Today, tracing standards have developed and we have much faster systems, making instrumentation less intrusive and data more valuable.Yuri Shkuro, the creator of Jaeger, a popular open-source distributed tracing system, delivers end-to-end coverage of the field in Mastering Distributed Tracing. Review the history and theoretical foundations of tracing; solve the data gathering problem through code instrumentation, with open standards like OpenTracing, W3C Trace Context, and OpenCensus; and discuss the benefits and applications of a distributed tracing infrastructure for understanding, and profiling, complex systems.

Unity 2017 Mobile Game Development. Build, deploy, and monetize games for Android and iOS with Unity

John P. Doran

Unity has established itself as an overpowering force for developing mobile games. If you love mobile games and want to learn how to make them but have no idea where to begin, then this book is just what you need. This book takes a clear, step-by-step approach to building an endless runner game using Unity with plenty of examples on how to create a game that is uniquely your own.Starting from scratch, you will build, set up, and deploy a simple game to a mobile device. You will learn to add touch gestures and design UI elements that can be used in both landscape and portrait mode at different resolutions. You will explore the best ways to monetize your game projects using Unity Ads and in-app purchases before you share your game information on social networks. Next, using Unity’s analytics tools you will be able to make your game better by gaining insights into how players like and use your game. Finally, you’ll learn how to publish your game on the iOS and Android App Stores for the world to see and play along.

Kali Linux Network Scanning Cookbook. A Step-by-Step Guide leveraging Custom Scripts and Integrated Tools in Kali Linux - Second Edition

Michael Hixon, Justin Hutchens

With the ever-increasing amount of data flowing in today’s world, information security has become vital to any application. This is where Kali Linux comes in. Kali Linux focuses mainly on security auditing and penetration testing. This step-by-step cookbook on network scanning trains you in important scanning concepts based on version 2016.2. It will enable you to conquer any network environment through a range of network scanning techniques and will also equip you to script your very own tools.Starting with the fundamentals of installing and managing Kali Linux, this book will help you map your target with a wide range of network scanning tasks, including discovery, port scanning, fingerprinting, and more. You will learn how to utilize the arsenal of tools available in Kali Linux to conquer any network environment. The book offers expanded coverage of the popular Burp Suite and has new and updated scripts for automating scanning and target exploitation. You will also be shown how to identify remote services, how to assess security risks, and how various attacks are performed. You will cover the latest features of Kali Linux 2016.2, which includes the enhanced Sparta tool and many other exciting updates.This immersive guide will also encourage the creation of personally scripted tools and the skills required to create them.

Bayesian Analysis with Python. Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ - Second Edition

Osvaldo Martin

The second edition of Bayesian Analysis with Python is an introduction to the main concepts of applied Bayesian inference and its practical implementation in Python using PyMC3, a state-of-the-art probabilistic programming library, and ArviZ, a new library for exploratory analysis of Bayesian models.The main concepts of Bayesian statistics are covered using a practical and computational approach. Synthetic and real data sets are used to introduce several types of models, such as generalized linear models for regression and classification, mixture models, hierarchical models, and Gaussian processes, among others. By the end of the book, you will have a working knowledge of probabilistic modeling and you will be able to design and implement Bayesian models for your own data science problems. After reading the book you will be better prepared to delve into more advanced material or specialized statistical modeling if you need to.

Practical Machine Learning Cookbook. Supervised and unsupervised machine learning simplified

Atul Tripathi

Machine learning has become the new black. The challenge in today’s world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations.The first half of the book provides recipes on fairly complex machine-learning systems, where you’ll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more.The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.

Matplotlib 3.0 Cookbook. Over 150 recipes to create highly detailed interactive visualizations using Python

Srinivasa Rao Poladi

Matplotlib provides a large library of customizable plots, along with a comprehensive set of backends. Matplotlib 3.0 Cookbook is your hands-on guide to exploring the world of Matplotlib, and covers the most effective plotting packages for Python 3.7. With the help of this cookbook, you'll be able to tackle any problem you might come across while designing attractive, insightful data visualizations. With the help of over 150 recipes, you'll learn how to develop plots related to business intelligence, data science, and engineering disciplines with highly detailed visualizations. Once you've familiarized yourself with the fundamentals, you'll move on to developing professional dashboards with a wide variety of graphs and sophisticated grid layouts in 2D and 3D. You'll annotate and add rich text to the plots, enabling the creation of a business storyline. In addition to this, you'll learn how to save figures and animations in various formats for downstream deployment, followed by extending the functionality offered by various internal and third-party toolkits, such as axisartist, axes_grid, Cartopy, and Seaborn. By the end of this book, you'll be able to create high-quality customized plots and deploy them on the web and on supported GUI applications such as Tkinter, Qt 5, and wxPython by implementing real-world use cases and examples.

Artificial Intelligence for Big Data. Complete guide to automating Big Data solutions using Artificial Intelligence techniques

Anand Deshpande, Manish Kumar

In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data.With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems.By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.