Hands-On Cryptography with Python
Cryptography is essential for protecting sensitive information, but it is often performed inadequately or incorrectly. Hands-On Cryptography with Python starts by showing you how to encrypt and evaluate your data. The book will then walk you through various data encryption methods,such as obfuscation, hashing, and strong encryption, and will show how you can attack cryptographic systems. You will learn how to create hashes, crack them, and will understand why they are so different from each other. In the concluding chapters, you will use three NIST-recommended systems: the Advanced Encryption Standard (AES), the Secure Hash Algorithm (SHA), and the Rivest-Shamir-Adleman (RSA). By the end of this book, you will be able to deal with common errors in encryption. ...
Cryptography is essential for protecting sensitive information, but it is often performed inadequately or incorrectly. Hands-On Cryptography with Python starts by showing you how to encrypt and evaluate your data. The book will then walk you through various data encryption methods,such as obfuscation, hashing, and strong encryption, and will show how you can attack cryptographic systems. You will learn how to create hashes, crack them, and will understand why they are so different from each other. In the concluding chapters, you will use three NIST-recommended systems: the Advanced Encryption Standard (AES), the Secure Hash Algorithm (SHA), and the Rivest-Shamir-Adleman (RSA). By the end of this book, you will be able to deal with common errors in encryption. ...
Pro Machine Learning Algorithms
Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, ...
Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R. You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, ...
Expert Geoserver
GeoServer is open source, server-side software written in Java that allows users to share and edit geospatial data. In this book, you'll start by learning how to develop a spatial analysis platform with web processing services. Then you'll see how to develop an algorithm by chaining together geospatial analysis processes, which you can share with anyone in the world. Next you'll delve into a very important technique to improve the speed of your map application - tile caching. Here, you'll understand how tile caching works, how to develop an effective tile cache-supported web service, and how to leverage tile caching in your OpenLayers web application. Further on, you'll explore important tweaks to produce a performant GeoServer-backed web mapping application. Moving on, you'll enable authentication on the frontend and backend to protect sensitive map data, and deliver sensitive data to your end user. Finally, you'll see how to put your web application into production in a secure ...
GeoServer is open source, server-side software written in Java that allows users to share and edit geospatial data. In this book, you'll start by learning how to develop a spatial analysis platform with web processing services. Then you'll see how to develop an algorithm by chaining together geospatial analysis processes, which you can share with anyone in the world. Next you'll delve into a very important technique to improve the speed of your map application - tile caching. Here, you'll understand how tile caching works, how to develop an effective tile cache-supported web service, and how to leverage tile caching in your OpenLayers web application. Further on, you'll explore important tweaks to produce a performant GeoServer-backed web mapping application. Moving on, you'll enable authentication on the frontend and backend to protect sensitive map data, and deliver sensitive data to your end user. Finally, you'll see how to put your web application into production in a secure ...
Beginning Java Data Structures and Algorithms
Learning about data structures and algorithms gives you a better insight on how to solve common programming problems. Most of the problems faced everyday by programmers have been solved, tried, and tested. By knowing how these solutions work, you can ensure that you choose the right tool when you face these problems. This book teaches you tools that you can use to build efficient applications. It starts with an introduction to algorithms and big O notation, later explains bubble, merge, quicksort, and other popular programming patterns. You'll also learn about data structures such as binary trees, hash tables, and graphs. The book progresses to advanced concepts, such as algorithm design paradigms and graph theory. By the end of the book, you will know how to correctly implement common algorithms and data structures within your applications. ...
Learning about data structures and algorithms gives you a better insight on how to solve common programming problems. Most of the problems faced everyday by programmers have been solved, tried, and tested. By knowing how these solutions work, you can ensure that you choose the right tool when you face these problems. This book teaches you tools that you can use to build efficient applications. It starts with an introduction to algorithms and big O notation, later explains bubble, merge, quicksort, and other popular programming patterns. You'll also learn about data structures such as binary trees, hash tables, and graphs. The book progresses to advanced concepts, such as algorithm design paradigms and graph theory. By the end of the book, you will know how to correctly implement common algorithms and data structures within your applications. ...
Data Science with SQL Server Quick Start Guide
SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment. You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm. ...
SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment. You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm. ...
Practical TLA+
Learn how to design complex, correct programs and fix problems before writing a single line of code. This book is a practical, comprehensive resource on TLA+ programming with rich, complex examples. Practical TLA+ shows you how to use TLA+ to specify a complex system and test the design itself for bugs. You'll learn how even a short TLA+ spec can find critical bugs. Start by getting your feet wet with an example of TLA+ used in a bank transfer system, to see how it helps you design, test, and build a better application. Then, get some fundamentals of TLA+ operators, logic, functions, PlusCal, models, and concurrency. Along the way you will discover how to organize your blueprints and how to specify distributed systems and eventual consistency. Finally, you'll put what you learn into practice with some working case study applications, applying TLA+ to a wide variety of practical problems: from algorithm performance and data structures to business code and MapReduce. After reading ...
Learn how to design complex, correct programs and fix problems before writing a single line of code. This book is a practical, comprehensive resource on TLA+ programming with rich, complex examples. Practical TLA+ shows you how to use TLA+ to specify a complex system and test the design itself for bugs. You'll learn how even a short TLA+ spec can find critical bugs. Start by getting your feet wet with an example of TLA+ used in a bank transfer system, to see how it helps you design, test, and build a better application. Then, get some fundamentals of TLA+ operators, logic, functions, PlusCal, models, and concurrency. Along the way you will discover how to organize your blueprints and how to specify distributed systems and eventual consistency. Finally, you'll put what you learn into practice with some working case study applications, applying TLA+ to a wide variety of practical problems: from algorithm performance and data structures to business code and MapReduce. After reading ...
Impractical Python Projects
Impractical Python Projects picks up where the complete beginner books leave off, expanding on existing concepts and introducing new tools that you'll use every day. And to keep things interesting, each project includes a zany twist featuring historical incidents, pop culture references, and literary allusions. You'll flex your problem-solving skills and employ Python's many useful libraries to do things like: Help James Bond crack a high-tech safe with a hill-climbing algorithm; Write haiku poems using Markov Chain Analysis; Use genetic algorithms to breed a race of gigantic rats; Crack the world's most successful military cipher using cryptanalysis; Foil corporate security with invisible electronic ink; Derive the anagram, "I am Lord Voldemort" using linguistical sieves; Plan your parents secure retirement with Monte Carlo simulation; Save the sorceress Zatanna from a stabby death using palingrams; Model the Milky Way and calculate our odds of detecting alien civilizations; Help t ...
Impractical Python Projects picks up where the complete beginner books leave off, expanding on existing concepts and introducing new tools that you'll use every day. And to keep things interesting, each project includes a zany twist featuring historical incidents, pop culture references, and literary allusions. You'll flex your problem-solving skills and employ Python's many useful libraries to do things like: Help James Bond crack a high-tech safe with a hill-climbing algorithm; Write haiku poems using Markov Chain Analysis; Use genetic algorithms to breed a race of gigantic rats; Crack the world's most successful military cipher using cryptanalysis; Foil corporate security with invisible electronic ink; Derive the anagram, "I am Lord Voldemort" using linguistical sieves; Plan your parents secure retirement with Monte Carlo simulation; Save the sorceress Zatanna from a stabby death using palingrams; Model the Milky Way and calculate our odds of detecting alien civilizations; Help t ...
Practical Java Machine Learning
Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You will be introduced to tools which can help you identify and manage your data including JSON, visualization, NoSQL databases, and cloud platforms including Google Cloud Platform and Amazon Web Services. Practical Java Machine Learning includes multiple projects, with particular focus on the Android mobile platform and features such as sensors, camera, and connectivity, each of which produce data that can power unique machine learning solutions. You will learn to build a variety of applications that demonstrate the capabilities of the Google Cloud Platform machine learning API, including data visualization for Java; document classification using the We ...
Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and must be considered throughout all phases of the project life cycle. Practical Java Machine Learning helps you understand the importance of data and how to organize it for use within your ML project. You will be introduced to tools which can help you identify and manage your data including JSON, visualization, NoSQL databases, and cloud platforms including Google Cloud Platform and Amazon Web Services. Practical Java Machine Learning includes multiple projects, with particular focus on the Android mobile platform and features such as sensors, camera, and connectivity, each of which produce data that can power unique machine learning solutions. You will learn to build a variety of applications that demonstrate the capabilities of the Google Cloud Platform machine learning API, including data visualization for Java; document classification using the We ...
Mastering Apache Cassandra 3.x, 3rd Edition
With ever-increasing rates of data creation, the demand for storing data fast and reliably becomes a need. Apache Cassandra is the perfect choice for building fault-tolerant and scalable databases. Mastering Apache Cassandra 3.x teaches you how to build and architect your clusters, configure and work with your nodes, and program in a high-throughput environment, helping you understand the power of Cassandra as per the new features. Once you've covered a brief recap of the basics, you'll move on to deploying and monitoring a production setup and optimizing and integrating it with other software. You'll work with the advanced features of CQL and the new storage engine in order to understand how they function on the server-side. You'll explore the integration and interaction of Cassandra components, followed by discovering features such as token allocation algorithm, CQL3, vnodes, lightweight transactions, and data modelling in detail. Last but not least you will get to grips with Apac ...
With ever-increasing rates of data creation, the demand for storing data fast and reliably becomes a need. Apache Cassandra is the perfect choice for building fault-tolerant and scalable databases. Mastering Apache Cassandra 3.x teaches you how to build and architect your clusters, configure and work with your nodes, and program in a high-throughput environment, helping you understand the power of Cassandra as per the new features. Once you've covered a brief recap of the basics, you'll move on to deploying and monitoring a production setup and optimizing and integrating it with other software. You'll work with the advanced features of CQL and the new storage engine in order to understand how they function on the server-side. You'll explore the integration and interaction of Cassandra components, followed by discovering features such as token allocation algorithm, CQL3, vnodes, lightweight transactions, and data modelling in detail. Last but not least you will get to grips with Apac ...
Machine Learning Fundamentals
As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem. The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters. By the end of this book, you will have gain all the skills required to start programming machine learning algorithms. ...
As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem. The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters. By the end of this book, you will have gain all the skills required to start programming machine learning algorithms. ...