Biznes IT

Apache Spark 2: Data Processing and Real-Time Analytics. Master complex big data processing, stream analytics, and machine learning with Apache Spark

Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla,...

Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform.You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools.By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle.This Learning Path includes content from the following Packt products:• Mastering Apache Spark 2.x by Romeo Kienzler• Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla• Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook

Apache Spark 2.x Cookbook. Over 70 cloud-ready recipes for distributed Big Data processing and analytics

Rishi Yadav

While Apache Spark 1.x gained a lot of traction and adoption in the early years, Spark 2.x delivers notable improvements in the areas of API, schema awareness, Performance, Structured Streaming, and simplifying building blocks to build better, faster, smarter, and more accessible big data applications. This book uncovers all these features in the form of structured recipes to analyze and mature large and complex sets of data.Starting with installing and configuring Apache Spark with various cluster managers, you will learn to set up development environments. Further on, you will be introduced to working with RDDs, DataFrames and Datasets to operate on schema aware data, and real-time streaming with various sources such as Twitter Stream and Apache Kafka. You will also work through recipes on machine learning, including supervised learning, unsupervised learning & recommendation engines in Spark.Last but not least, the final few chapters delve deeper into the concepts of graph processing using GraphX, securing your implementations, cluster optimization, and troubleshooting.

Apache Spark 2.x for Java Developers. Explore big data at scale using Apache Spark 2.x Java APIs

Sourav Gulati, Sumit Kumar

Apache Spark is the buzzword in the big data industry right now, especially with the increasing need for real-time streaming and data processing. While Spark is built on Scala, the Spark Java API exposes all the Spark features available in the Scala version for Java developers. This book will show you how you can implement various functionalities of the Apache Spark framework in Java, without stepping out of your comfort zone.The book starts with an introduction to the Apache Spark 2.x ecosystem, followed by explaining how to install and configure Spark, and refreshes the Java concepts that will be useful to you when consuming Apache Spark's APIs. You will explore RDD and its associated common Action and Transformation Java APIs, set up a production-like clustered environment, and work with Spark SQL. Moving on, you will perform near-real-time processing with Spark streaming, Machine Learning analytics with Spark MLlib, and graph processing with GraphX, all using various Java packages.By the end of the book, you will have a solid foundation in implementing components in the Spark framework in Java to build fast, real-time applications.

Apache Spark 2.x Machine Learning Cookbook. Over 100 recipes to simplify machine learning model implementations with Spark

Siamak Amirghodsi, Mohammed Guller, Shuen Mei, Meenakshi...

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks.This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.

Apache Spark Deep Learning Cookbook. Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow

Ahmed Sherif, Amrith Ravindra, Michal Malohlava, Adnan...

Organizations these days need to integrate popular big data tools such as Apache Spark with highly efficient deep learning libraries if they’re looking to gain faster and more powerful insights from their data. With this book, you’ll discover over 80 recipes to help you train fast, enterprise-grade, deep learning models on Apache Spark.Each recipe addresses a specific problem, and offers a proven, best-practice solution to difficulties encountered while implementing various deep learning algorithms in a distributed environment. The book follows a systematic approach, featuring a balance of theory and tips with best practice solutions to assist you with training different types of neural networks such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). You’ll also have access to code written in TensorFlow and Keras that you can run on Spark to solve a variety of deep learning problems in computer vision and natural language processing (NLP), or tweak to tackle other problems encountered in deep learning.By the end of this book, you'll have the skills you need to train and deploy state-of-the-art deep learning models on Apache Spark.

Apache Spark for Machine Learning. Build and deploy high-performance big data AI solutions for large-scale clusters

Deepak Gowda

In the world of big data, efficiently processing and analyzing massive datasets for machine learning can be a daunting task. Written by Deepak Gowda, a data scientist with over a decade of experience and 30+ patents, this book provides a hands-on guide to mastering Spark’s capabilities for efficient data processing, model building, and optimization. With Deepak’s expertise across industries such as supply chain, cybersecurity, and data center infrastructure, he makes complex concepts easy to follow through detailed recipes.This book takes you through core machine learning concepts, highlighting the advantages of Spark for big data analytics. It covers practical data preprocessing techniques, including feature extraction and transformation, supervised learning methods with detailed chapters on regression and classification, and unsupervised learning through clustering and recommendation systems. You’ll also learn to identify frequent patterns in data and discover effective strategies to deploy and optimize your machine learning models. Each chapter features practical coding examples and real-world applications to equip you with the knowledge and skills needed to tackle complex machine learning tasks.By the end of this book, you’ll be ready to handle big data and create advanced machine learning models with Apache Spark.

Apache Spark Quick Start Guide. Quickly learn the art of writing efficient big data applications with Apache Spark

Shrey Mehrotra, Akash Grade

Apache Spark is a ?exible framework that allows processing of batch and real-time data. Its unified engine has made it quite popular for big data use cases. This book will help you to get started with Apache Spark 2.0 and write big data applications for a variety of use cases.It will also introduce you to Apache Spark – one of the most popular Big Data processing frameworks. Although this book is intended to help you get started with Apache Spark, but it also focuses on explaining the core concepts. This practical guide provides a quick start to the Spark 2.0 architecture and its components. It teaches you how to set up Spark on your local machine. As we move ahead, you will be introduced to resilient distributed datasets (RDDs) and DataFrame APIs, and their corresponding transformations and actions. Then, we move on to the life cycle of a Spark application and learn about the techniques used to debug slow-running applications. You will also go through Spark’s built-in modules for SQL, streaming, machine learning, and graph analysis.Finally, the book will lay out the best practices and optimization techniques that are key for writing efficient Spark applications. By the end of this book, you will have a sound fundamental understanding of the Apache Spark framework and you will be able to write and optimize Spark applications.

Apache Superset Quick Start Guide. Develop interactive visualizations by creating user-friendly dashboards

Shashank Shekhar

Apache Superset is a modern, open source, enterprise-ready business intelligence (BI) web application. With the help of this book, you will see how Superset integrates with popular databases like Postgres, Google BigQuery, Snowflake, and MySQL. You will learn to create real time data visualizations and dashboards on modern web browsers for your organization using Superset.First, we look at the fundamentals of Superset, and then get it up and running. You'll go through the requisite installation, configuration, and deployment. Then, we will discuss different columnar data types, analytics, and the visualizations available. You'll also see the security tools available to the administrator to keep your data safe.You will learn how to visualize relationships as graphs instead of coordinates on plain orthogonal axes. This will help you when you upload your own entity relationship dataset and analyze the dataset in new, different ways. You will also see how to analyze geographical regions by working with location data.Finally, we cover a set of tutorials on dashboard designs frequently used by analysts, business intelligence professionals, and developers.

Application Lifecycle Management on Microsoft Power Platform. A comprehensive guide to managing the deployment of your solutions

Benedikt Bergmann, Scott Durow

Managing Power Platform solutions manually can be challenging and time-consuming, as is application lifecycle management (ALM), which encompasses governance, development, and maintenance. This book provides comprehensive coverage of ALM, addressing planning, development, testing, deployment, and maintenance. Drawing on his extensive experience as a Power Platform consultant and Microsoft MVP, Benedikt Bergmann simplifies complex topics, making them accessible and easy to grasp.From planning and designing applications to deploying and maintaining them, this book provides step-by-step instructions, best practices, and real-world examples to effectively manage the entire application lifecycle. You’ll gain insights into optimizing Power Platform's toolbox, including Power Apps, Power Automate, Power Pages, and Power Virtual Agents, for seamless collaboration, agile development, and rapid application delivery. You’ll also implement best practices for version control, code management, and collaboration using the Microsoft Power Platform.By the end of this book, you’ll be equipped with the knowledge and skills to effectively manage the entire application lifecycle, accelerate development cycles, and deliver exceptional solutions with the Microsoft Power Platform.

Applied Data Science with Python and Jupyter. Use powerful industry-standard tools to unlock new, actionable insights from your data

Alex Galea

Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations.

Applied Data Visualization with R and ggplot2. Create useful, elaborate, and visually appealing plots

Dr. Tania Moulik

Applied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques.You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries.By the end of this book, you’ll have created data visualizations that will impress your clients.

Applied Data Visualization with R and ggplot2. Create useful, elaborate, and visually appealing plots

Dr. Tania Moulik

Applied Data Visualization with R and ggplot2 introduces you to the world of data visualization by taking you through the basic features of ggplot2. To start with, you’ll learn how to set up the R environment, followed by getting insights into the grammar of graphics and geometric objects before you explore the plotting techniques.You’ll discover what layers, scales, coordinates, and themes are, and study how you can use them to transform your data into aesthetical graphs. Once you’ve grasped the basics, you’ll move on to studying simple plots such as histograms and advanced plots such as superimposing and density plots. You’ll also get to grips with plotting trends, correlations, and statistical summaries.By the end of this book, you’ll have created data visualizations that will impress your clients.

Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques

Balu Nair, Sumit Ranjan, Dr. S. Senthamilarasu

Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving.By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.

Applied Deep Learning on Graphs. Leverage graph data for business applications using specialized deep learning architectures

Lakshya Khandelwal, Subhajoy Das

With their combined expertise spanning cutting-edge AI product development at industry giants such as Walmart, Adobe, Samsung, and Arista Networks, Lakshya and Subhajoy provide real-world insights into the transformative world of graph neural networks (GNNs).This book demystifies GNNs, guiding you from foundational concepts to advanced techniques and real-world applications. You’ll see how graph data structures power today’s interconnected world, why specialized deep learning approaches are essential, and how to address challenges with existing methods. You’ll start by dissecting early graph representation techniques such as DeepWalk and node2vec. From there, the book takes you through popular GNN architectures, covering graph convolutional and attention networks, autoencoder models, LLMs, and technologies such as retrieval augmented generation on graph data. With a strong theoretical grounding, you’ll seamlessly navigate practical implementations, mastering the critical topics of scalability, interpretability, and application domains such as NLP, recommendations, and computer vision.By the end of this book, you’ll have mastered the underlying ideas and practical coding skills needed to innovate beyond current methods and gained strategic insights into the future of GNN technologies.

Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras

Ritesh Bhagwat , Mahla Abdolahnejad , Matthew...

Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.

Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

David S. Jordan

Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.

Applied Unsupervised Learning with R. Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

Alok Malik, Bradford Tuckfield

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.

Architects of Intelligence. The truth about AI from the people building it

Martin Ford

Financial Times Best Books of the Year 2018TechRepublic Top Books Every Techie Should ReadHow will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances?Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community.Martin has wide-ranging conversations with twenty-three of the world's foremost researchers and entrepreneurs working in AI and robotics: Demis Hassabis (DeepMind), Ray Kurzweil (Google), Geoffrey Hinton (Univ. of Toronto and Google), Rodney Brooks (Rethink Robotics), Yann LeCun (Facebook) , Fei-Fei Li (Stanford and Google), Yoshua Bengio (Univ. of Montreal), Andrew Ng (AI Fund), Daphne Koller (Stanford), Stuart Russell (UC Berkeley), Nick Bostrom (Univ. of Oxford), Barbara Grosz (Harvard), David Ferrucci (Elemental Cognition), James Manyika (McKinsey), Judea Pearl (UCLA), Josh Tenenbaum (MIT), Rana el Kaliouby (Affectiva), Daniela Rus (MIT), Jeff Dean (Google), Cynthia Breazeal (MIT), Oren Etzioni (Allen Institute for AI), Gary Marcus (NYU), and Bryan Johnson (Kernel).Martin Ford is a prominent futurist, and author of Financial Times Business Book of the Year, Rise of the Robots. He speaks at conferences and companies around the world on what AI and automation might mean for the future.Meet the minds behind the AI superpowers as they discuss the science, business and ethics of modern artificial intelligence. Read James Manyika’s thoughts on AI analytics, Geoffrey Hinton’s breakthroughs in AI programming and development, and Rana el Kaliouby’s insights into AI marketing. This AI book collects the opinions of the luminaries of the AI business, such as Stuart Russell (coauthor of the leading AI textbook), Rodney Brooks (a leader in AI robotics), Demis Hassabis (chess prodigy and mind behind AlphaGo), and Yoshua Bengio (leader in deep learning) to complete your AI education and give you an AI advantage in 2019 and the future.

Architektura Lean w projektach Agile

James O. Coplien, Gertrud Bjornvig

Programuj i organizuj kod z metodyką Lean! Tempo rozwoju aplikacji wymusza stosowanie elastycznych sposobów wytwarzania oprogramowania. Książka ta została poświęcona architekturze Lean, która usprawni ten proces dzięki nowatorskiemu podejściu. Wykorzystaj je i przygotuj swoją aplikację na zmiany funkcjonalne, by użytkownicy mogli w pełni wykorzystać jej potencjał! W trakcie lektury zapoznasz się z duchem Agile i Lean oraz przydzielisz najważniejsze role członkom projektu. Po tym niezwykle interesującym wstępie rozpoczniesz pasjonującą podróż po świecie architektury Lean. Dowiesz się, czym jest system, jak podzielić projekt na części i wybrać jego styl. W kolejnych rozdziałach zorganizujesz swój kod i przetestujesz zaprojektowaną architekturę. Znajdziesz tu wiele przykładów, które w najlepszy sposób przedstawiają założenia i intencje architektury Lean, z dużym naciskiem na sam kod. To obowiązkowa lektura dla wszystkich programistów i projektantów systemów informatycznych. Dzięki tej książce: poznasz filozofię Agile i Lean zbudujesz kod odporny na zmiany zrozumiesz paradygmat DCI poznasz współczesne metody wytwarzania oprogramowania! Twój przewodnik po architekturze Lean!

Architektura oprogramowania i podejmowanie decyzji: Wykorzystywanie przywództwa, technologii i zarządzania produktem do budowy świetnych produktów

Srinath Perera

Nadrzędnym celem systemów oprogramowania (a więc i architektury oprogramowania) jest budowanie systemów spełniających standardy jakości i zapewniających najwyższy zwrot z inwestycji (ROI) w dłuższej perspektywie lub w określonym przedziale czasu. Świetny produkt wymaga połączenia technologii, przywództwa i zarządzania produktem (wliczając w to UX). Przywództwo polega przede wszystkim na zarządzaniu niepewnością i dokonywaniu prawidłowej oceny sytuacji. Aby tworzyć świetne produkty, liderzy techniczni muszą łączyć technologię, przywództwo i wiedzę z zakresu zarządzania produktem, a także podejmować właściwe decyzje. Wiele błędów technicznych wynika z luki między wiedzą na temat tych trzech elementów i oceną sytuacji. W książce Architektura oprogramowania i podejmowanie decyzji Srinath Perera wyjaśnia zasady i koncepcje, które architekci oprogramowania muszą dogłębnie zrozumieć, oraz sposoby stosowania tych zasad do zarządzania niepewnością. Pytania i zasady omówione w tej książce pomagają zarządzać niepewnością podczas tworzenia architektury oprogramowania i zapewniają ramy do podejmowania decyzji. Ta książka jest przeznaczona dla wszystkich liderów technicznych w branży oprogramowania, którzy dokonują całościowej oceny budowanych przez siebie systemów, jak również dla przyszłych liderów uczących się tego rzemiosła. Zrozum znaczenie podejmowania zdecydowanych decyzji na przykładach wielkich liderów technicznych, takich jak bracia Wright i Kelly Johnson Wykorzystaj pięć kluczowych pytań i siedem ważnych zasad, aby zrozumieć niepewności podczas projektowania i podjąć strategiczne decyzje architektoniczne Podejdź do projektu systematycznie, najpierw na poziomie makro, a następnie na poziomie indywidualnej usługi Wykorzystaj wiedzę z zakresu przywództwa, aby podejmować lepsze decyzje dotyczące architektury oprogramowania. Myśl głęboko, ale implementuj powoli. Srinath Perera jest głównym architektem w WSO2. Przez ostatnich 20 lat był deweloperem rozwiązań open source Apache. Srinath odegrał znaczącą rolę w projektowaniu Apache Axis2, Apache Airavata, WSO2 CEP (Siddhi) i WSO2 Choreo. Zrecenzował architektury dla ponad 10 projektów i ponad 100 wydań.

Artificial Intelligence and Machine Learning Fundamentals. Develop real-world applications powered by the latest AI advances

Zsolt Nagy

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law.By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!

Artificial Intelligence By Example. Develop machine intelligence from scratch using real artificial intelligence use cases

Denis Rothman, Nicolas GUET, Jérémie ROTHMAN

Artificial intelligence has the potential to replicate humans in every field. Artificial Intelligence By Example serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies.Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks.You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own.By the end of this book, you will have understood the fundamentals of AI and worked through a number of case studies that will help you develop your business vision.

Artificial Intelligence. Ethical, social, and security impacts for the present and the future

IT Governance Publishing, Dr. Julie E. Mehan

This book offers an in-depth exploration of Artificial Intelligence (AI), from its origins to the ethical and societal challenges it presents today. It provides a comprehensive understanding of AI’s impact on human interaction, collaboration, privacy, and security. Through analyzing both opportunities and risks, the book emphasizes the ethical concerns surrounding AI, such as bias, privacy violations, and security threats.Chapters explore AI’s transformative role in cybersecurity, misinformation, and human-machine collaboration, highlighting its implications for job markets and human relationships. Real-world examples illustrate how AI can drive progress or cause harm. The ethical dilemmas around AI, including its use in surveillance and decision-making, are thoroughly examined, presenting challenges central to modern technology.Looking ahead, the book offers a forward-thinking perspective on AI’s future, discussing emerging trends and the need for responsible policy-making. It concludes by addressing how society can prepare for AI’s continued growth, offering strategies for navigating the evolving landscape. With practical insights and deep analysis, this book helps readers grasp AI’s profound implications for our future.

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

Anand Deshpande, Manish Kumar, Albenzo Coletta, Giancarlo...

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.

Artificial Intelligence for Robotics. Build intelligent robots that perform human tasks using AI techniques

Francis X. Govers III

Artificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills.As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. In the concluding chapters, you will learn about path planning and goal-oriented programming, which will help your robot prioritize tasks.By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence.