Notes on Randomized Algorithms
Lecture notes for the Yale Computer Science course CPSC 469/569 Randomized Algorithms. Suitable for use as a supplementary text for an introductory graduate or advanced undergraduate course on randomized algorithms. Discusses tools from probability theory, including random variables and expectations, union bound arguments, concentration bounds, applications of martingales and Markov chains, and the Lovasz Local Lemma. Algorithmic topics include analysis of classic randomized algorithms such as Quicksort and Hoare's FIND, randomized tree data structures, hashing, Markov chain Monte Carlo sampling, randomized approximate counting, derandomization, quantum computing, and some examples of randomized distributed algorithms. ...
Lecture notes for the Yale Computer Science course CPSC 469/569 Randomized Algorithms. Suitable for use as a supplementary text for an introductory graduate or advanced undergraduate course on randomized algorithms. Discusses tools from probability theory, including random variables and expectations, union bound arguments, concentration bounds, applications of martingales and Markov chains, and the Lovasz Local Lemma. Algorithmic topics include analysis of classic randomized algorithms such as Quicksort and Hoare's FIND, randomized tree data structures, hashing, Markov chain Monte Carlo sampling, randomized approximate counting, derandomization, quantum computing, and some examples of randomized distributed algorithms. ...
Critical Data Literacy
A short course for students to increase their proficiency in analyzing and interpreting data visualizations. By completing this short course students will be able to explain the importance of data literacy, identify data visualization issues in order to improve their own skills in data story-telling. The intended outcome of this course is to help students become more discerning and critical users of data, graphs, charts and infographics. The need to understand data visualizations has never been more important. Every day we are inundated with more data, graphs and charts. Some of these data visualizations are well-designed and easy to understand, and others are confusing and misleading. Data literacy is often framed as a set of skills for data professionals, but we believe data literacy is for everyone. Everyone can benefit from improving their understanding of how data is created and their ability to analyze and interpret data. In this book, we will introduce the key stages in ...
A short course for students to increase their proficiency in analyzing and interpreting data visualizations. By completing this short course students will be able to explain the importance of data literacy, identify data visualization issues in order to improve their own skills in data story-telling. The intended outcome of this course is to help students become more discerning and critical users of data, graphs, charts and infographics. The need to understand data visualizations has never been more important. Every day we are inundated with more data, graphs and charts. Some of these data visualizations are well-designed and easy to understand, and others are confusing and misleading. Data literacy is often framed as a set of skills for data professionals, but we believe data literacy is for everyone. Everyone can benefit from improving their understanding of how data is created and their ability to analyze and interpret data. In this book, we will introduce the key stages in ...
Building an Effective Data Science Practice
Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science - classes of data science problems, data science techniques and their applications - and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practice provides a common base ...
Gain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science - classes of data science problems, data science techniques and their applications - and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practice provides a common base ...
Cryptography and Cryptanalysis in Java
Here is your in-depth guide to cryptography and cryptanalysis in Java. This book includes challenging cryptographic solutions that are implemented in Java 17 and Jakarta EE 10. It provides a robust introduction to Java 17's new features and updates, a roadmap for Jakarta EE 10 security mechanisms, a unique presentation of the "hot points" (advantages and disadvantages) from the Java Cryptography Architecture (JCA), and more. The book dives into the classical simple cryptosystems that form the basis of modern cryptography, with fully working solutions (encryption/decryption operations). Pseudo-random generators are discussed as well as real-life implementations. Hash functions are covered along with practical cryptanalysis methods and attacks, asymmetric and symmetric encryption systems, signature and identification schemes. The book wraps up with a presentation of lattice-based cryptography and the NTRU framework library. Modern encryption schemes for cloud and big data environme ...
Here is your in-depth guide to cryptography and cryptanalysis in Java. This book includes challenging cryptographic solutions that are implemented in Java 17 and Jakarta EE 10. It provides a robust introduction to Java 17's new features and updates, a roadmap for Jakarta EE 10 security mechanisms, a unique presentation of the "hot points" (advantages and disadvantages) from the Java Cryptography Architecture (JCA), and more. The book dives into the classical simple cryptosystems that form the basis of modern cryptography, with fully working solutions (encryption/decryption operations). Pseudo-random generators are discussed as well as real-life implementations. Hash functions are covered along with practical cryptanalysis methods and attacks, asymmetric and symmetric encryption systems, signature and identification schemes. The book wraps up with a presentation of lattice-based cryptography and the NTRU framework library. Modern encryption schemes for cloud and big data environme ...
Machine Learning with PySpark, 2nd Edition
Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems. Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. You'll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. You'll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You'll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering ...
Master the new features in PySpark 3.1 to develop data-driven, intelligent applications. This updated edition covers topics ranging from building scalable machine learning models, to natural language processing, to recommender systems. Machine Learning with PySpark, Second Edition begins with the fundamentals of Apache Spark, including the latest updates to the framework. Next, you will learn the full spectrum of traditional machine learning algorithm implementations, along with natural language processing and recommender systems. You'll gain familiarity with the critical process of selecting machine learning algorithms, data ingestion, and data processing to solve business problems. You'll see a demonstration of how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forests. You'll also learn how to automate the steps using Spark pipelines, followed by unsupervised models such as K-means and hierarchical clustering ...
Open Data Structures
Offered as an introduction to the field of data structures and algorithms, Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs. Focusing on a mathematically rigorous approach that is fast, practical, and efficient, Morin clearly and briskly presents instruction along with source code. Analyzed and implemented in Java, the data structures presented in the book include stacks, queues, deques, and lists implemented as arrays and linked-lists; space-efficient implementations of lists; skip lists; hash tables and hash codes; binary search trees including treaps, scapegoat trees, and red-black trees; integer searching structures including binary tries, x-fast tries, and y-fast tries; heaps, including implicit binary heaps and randomized meldable heaps; graphs, including adjacency matrix and adjacency list representations; and B-trees. A modern treatment of ...
Offered as an introduction to the field of data structures and algorithms, Open Data Structures covers the implementation and analysis of data structures for sequences (lists), queues, priority queues, unordered dictionaries, ordered dictionaries, and graphs. Focusing on a mathematically rigorous approach that is fast, practical, and efficient, Morin clearly and briskly presents instruction along with source code. Analyzed and implemented in Java, the data structures presented in the book include stacks, queues, deques, and lists implemented as arrays and linked-lists; space-efficient implementations of lists; skip lists; hash tables and hash codes; binary search trees including treaps, scapegoat trees, and red-black trees; integer searching structures including binary tries, x-fast tries, and y-fast tries; heaps, including implicit binary heaps and randomized meldable heaps; graphs, including adjacency matrix and adjacency list representations; and B-trees. A modern treatment of ...
Essential Math for Data Science
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning; Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon; Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance; Manipulate vectors and matrices and perform matrix decomposition; Integrate and build upon incremental ...
Master the math needed to excel in data science, machine learning, and statistics. In this book author Thomas Nield guides you through areas like calculus, probability, linear algebra, and statistics and how they apply to techniques like linear regression, logistic regression, and neural networks. Along the way you'll also gain practical insights into the state of data science and how to use those insights to maximize your career. Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning; Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon; Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance; Manipulate vectors and matrices and perform matrix decomposition; Integrate and build upon incremental ...
Practical Simulations for Machine Learning
Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models.That's just the beginning. With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine; Use a game engine to synthesize images for use as training data; Create simulation environments designed for training deep reinforcement learning and imitation learning models; Us ...
Simulation and synthesis are core parts of the future of AI and machine learning. Consider: programmers, data scientists, and machine learning engineers can create the brain of a self-driving car without the car. Rather than use information from the real world, you can synthesize artificial data using simulations to train traditional machine learning models.That's just the beginning. With this practical book, you'll explore the possibilities of simulation- and synthesis-based machine learning and AI, concentrating on deep reinforcement learning and imitation learning techniques. AI and ML are increasingly data driven, and simulations are a powerful, engaging way to unlock their full potential. You'll learn how to: Design an approach for solving ML and AI problems using simulations with the Unity engine; Use a game engine to synthesize images for use as training data; Create simulation environments designed for training deep reinforcement learning and imitation learning models; Us ...
Inside Deep Learning
Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped - you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. Deep learning doesn't have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don't have to be a mathematics expert or a senior data scientist to grasp what's going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You'll explore deep learning concepts and tools through plain language explanations, ...
Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped - you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. Deep learning doesn't have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don't have to be a mathematics expert or a senior data scientist to grasp what's going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You'll explore deep learning concepts and tools through plain language explanations, ...
Beginning Data Science in R 4, 2nd Edition
Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. ...
Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. ...