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Trends in Deep Learning Methodologies...Algorithms, Applications, and Systems

Book

Brief

Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models.

Advanced ASP.NET Core 3.0 Security... Understanding Hacks, Attacks, and Vulnerabilities

Book

Brief

The ASP.NET Core framework does a good job in preventing certain types of attacks from happening, but there are many more non-trivial projects that invariably require developers to think “outside the box.” For that, there is very little guidance on how to safely venture beyond the simple use cases. And worse, there is a lot of bad advice online on how to implement functionality, be it encrypting unsafely hard-code parameters that need to be generated at runtime, to articles that advocate for certain solutions that are vulnerable to obvious injection attacks. This book aims to train developers to avoid these problems. Unlike the vast majority of security books that are targeted to network administrators, system administrators, or managers, this book is targeted specifically to ASP.NET developers. The book begins by teaching developers how ASP.NET Core works behind the scenes, then talks about how various attacks are performed and how to prevent them. Finally, it dives into the concepts a developer needs to know to do some testing on their own without the help of a security professional.

Application Delivery and Load Balancing in Microsoft Azure...

Brief

Book

With more and more companies moving on-premises applications to the cloud, software and cloud solution architects alike are busy investigating ways to improve load balancing, performance, security, and high availability for workloads. This practical book describes Microsoft Azure’s load balancing options and explains how NGINX can contribute to a comprehensive solution. Cloud solution architect Arlan Nugara takes you through the steps necessary to design a practical solution for your network. Software developers and technical managers will learn how these technologies have a direct impact on application development and architecture. While the examples are specific to Azure, these load balancing concepts and implementations also apply to cloud providers such as AWS, Google Cloud Platform, DigitalOcean, and IBM Cloud.

ASP.NET Core in Action, Second Edition

Book

Brief

Fully updated to ASP.NET 3.1, ASP.NET Core in Action, Second Edition is a hands-on primer to building cross-platform web applications with your C# and .NET skills. Even if you’ve never worked with ASP.NET you’ll start creating productive cross-platform web apps fast! ASP.NET Core in Action, Second Edition opens up the world of cross-platform web development with ASP.NET Core. You’ll start with a crash course in .NET Core, then you’ll begin to build amazing web applications step by step, systematically adding essential features. Along the way, you’ll mix in important process steps like testing, multiplatform deployment, and security.

Which Cloud?... A Developer's and Architect's Guide...

Brief

Book

The book addresses three key challenges: Understanding equivalent services to migrate to from a different cloud platform. Understanding the service and feature-level values gained across the different cloud platforms. Understanding effective ways to plan for or architect a cross-cloud solution. The authors begin with service-level comparisons, but they also dig into feature-level comparisons. They compare all three platforms together, to make it simple to understand the differences between all three cloud platforms. They use matrices, diagrams, and conceptual explanations to walk the reader through the comparisons. By the end of this book, you’ll have reached the next point in your cloud solution journey. Maybe that means you’re a student… you’ll now have a much stronger high-level view of all the platforms (and understand how to compare them). Or, if you’re a seasoned architect or developer, you’ll now have a better understanding of how to do your job… whether it’s for cross-cloud solutions, migrations, decision-making, or simply improved communication with coworkers.

BigQuery for Data Warehousing...Managed Data Analysis

Brief

Book

BigQuery is a managed cloud platform from Google that provides enterprise data warehousing and reporting capabilities. Part I of this book shows you how to design and provision a data warehouse in the BigQuery platform.  Part II teaches you how to load and stream your operational data into the warehouse to make it ready for analysis and reporting.  Parts III and IV cover querying and maintaining, helping you keep your information relevant with other Google Cloud Platform services and advanced BigQuery.  Part V takes reporting to the next level by showing you how to create dashboards to provide at-a-glance visual representations of your business situation.  Part VI provides an introduction to data science with BigQuery, covering machine learning and Jupyter notebooks.

Data Mining...Concepts, Models, Methods, and Algorithms

Book

Brief

Advances in deep learning technology have opened an entire new spectrum of applications. The author―a noted expert on the topic―explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter. Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.

Machine Learning Refined: Foundations, Algorithms, and Applications

Book/th>

Brief

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.

Designing Data-Intensive Applications...The Big Ideas Behind

Brief

Book

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, the author  helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications.

Python for Data Analysis...Wrangling with Pandas, NumPy, and IPython

Brief

Book

Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.

Data Mining for Business Analytics...Concepts from R

Brief

Book

Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. This book is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.

Machine Learning...A Bayesian and Optimization Perspective

Book

Brief

This book gives a unified perspective on machine learning by covering both pillars of supervised learning, namely regression and classification. The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines,  Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth. This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization.

Machine Learning...An Algorithmic Perspective

Book

Brief

Remedying this deficiency, this edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.  New to the Second Edition  Revision of the support vector machine material, including a simple implementation for experiments  New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron  Improved code, including better use of naming conventions in Python  Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code.  Each chapter includes detailed examples along with further reading and problems.  All of the code used to create the examples is available on the author’s website.

Mastering Machine Learning Algorithms...Expert techniques

Book

Brief

This masterpiece helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.

Data-Driven Science and Engineering...

Brief

Book

Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.

Artificial Intelligenc...Foundations of Computational Agents

Brief

Book

Artificial intelligence, including machine learning, has emerged as a transformational science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents presents AI using a coherent framework to study the design of intelligent computational agents. By showing how the basic approaches fit into a multidimensional design space, readers learn the fundamentals without losing sight of the bigger picture. The new edition also features expanded coverage on machine learning material, as well as on the social and ethical consequences of AI and ML. The book balances theory and experiment, showing how to link them together, and develops the science of AI together with its engineering applications. Although structured as an undergraduate and graduate textbook, the book's straightforward, self-contained style will also appeal to an audience of professionals, researchers, and independent learners. The second edition is well-supported by strong pedagogical features and online resources to enhance student comprehension.

A Human Algorithm...Artificial Intelligence Is Redefining Who We Are

Brief

Book

This work of art examines the immense impact intelligent technology will have on humanity. These machines, while challenging our personal beliefs and our socioeconomic world order, also have the potential to transform our health and well-being, alleviate poverty and suffering, and reveal the mysteries of intelligence and consciousness. International human rights attorney Flynn Coleman deftly argues that it is critical that we instill values, ethics, and morals into our robots, algorithms, and other forms of AI. Equally important, we need to develop and implement laws, policies, and oversight mechanisms to protect us from tech’s insidious threats.  To realize AI’s transcendent potential, Coleman advocates for inviting a diverse group of voices to participate in designing our intelligent machines and using our moral imagination to ensure that human rights, empathy, and equity are core principles of emerging technologies. Ultimately, A Human Algorithm is a clarion call for building a more humane future and moving conscientiously into a new frontier of our own design.

Machine Learning Refined...

Brief

Book

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.

Grokking Artificial Intelligence Algorithms

Brief

Book

Grokking Artificial Intelligence Algorithms is a fully-illustrated and interactive tutorial guide to the different approaches and algorithms that underpin AI. Written in simple language and with lots of visual references and hands-on examples, readers learn the concepts, terminology, and theory they need to effectively incorporate AI algorithms into their applications. Grokking Artificial Intelligence Algorithms uses simple language, jargon-busting explanations, and hand-drawn diagrams to open up complex algorithms. Don't worry if you aren't a calculus wunderkind; you'll need only the algebra you picked up in math class.