Future Human
Future Human is a visionary exploration of the intersection between humanity and the rapidly evolving field of artificial intelligence. Authored by Tony Tan, a distinguished figure in the artificial intelligence (AI) and technology industry, the book promises to unravel the complexities of AI, its integration into daily life, and its profound implications for the future of human civilisation.It addresses the critical role of human consciousness in an AI-driven world, the ethical frameworks necessary to guide AI development, and the regulatory challenges that accompany AI's integration into society. Through engaging storytelling and insightful analysis, Future Human aims to demystify AI, making it accessible to a broad audience while highlighting the importance of ethical considerations and human oversight.Tony's perspective - enriched by his extensive experience and achievements in the AI field - provides a unique lens through which readers can explore the potential of AI to enhance human capabilities, transform industries, and redefine societal norms. More than just a book, it is a call to action for thoughtful engagement with AI; encouraging readers to participate in shaping a future where technology amplifies human potential without compromising ethics or human dignity.
Essentials of AI for Beginners
Harness AI's potential quickly and easily for your career and creative endeavors. Gain practical skills that will turn your AI curiosity into confidence!Are you: ...intrigued about AI but overwhelmed by the jargon and rapid pace of change?...concerned about being left behind because you don't know AI?...eager to learn but short on time?...excited about using AI for work and creative projects but are unsure where to begin?...worried about AI's potential impact on your life and society as a whole?If any of this sounds like you, Essentials of AI for Beginners is the guide you need. This book breaks down the complexities of AI in clear, approachable language, making it enjoyable and easy to understand-without taking up all your time.Why choose this book?Well-curated content offers a comprehensive exploration of AI while remaining beginner-friendly.Four personalized learning tracks let you focus on what matters most to you.Hands-on learning with step-by-step tutorials and activities.Relatable analogies and case studies make AI concepts easy to grasp.No tech background needed.Exclusive bonus material to take your learning to the next level.By the end, you'll feel confident about embracing AI and using it at work, in your hobbies, and in everyday life.
Essentials of AI for Beginners
Harness AI's potential quickly and easily for your career and creative endeavors. Gain practical skills that will turn your AI curiosity into confidence!Are you: ...intrigued about AI but overwhelmed by the jargon and rapid pace of change?...concerned about being left behind because you don't know AI?...eager to learn but short on time?...excited about using AI for work and creative projects but are unsure where to begin?...worried about AI's potential impact on your life and society as a whole?If any of this sounds like you, Essentials of AI for Beginners is the guide you need. This book breaks down the complexities of AI in clear, approachable language, making it enjoyable and easy to understand-without taking up all your time.Essentials of AI for Beginners breaks down the complexities of AI in clear, approachable language, making it enjoyable and easy to understand.Why choose this book?Well-curated content offers a comprehensive exploration of AI while remaining beginner-friendly.Four personalized learning tracks let you focus on what matters most to you.Hands-on learning with step-by-step tutorials and activities.Relatable analogies and case studies make AI concepts easy to grasp.No tech background is needed.Exclusive bonus material to take your learning to the next level.
Artificial Intelligence and Digital Twin Technology
This book constitutes papers from the 1st International Conference on Artificial Intelligence and Digital Twin Technology, IconAIDTT 2023, held in Sivakasi, India, April 26-28, 2023. The 12 full papers and the 1 short paper included in this volume were carefully reviewed and selected from 41 submissions. The papers cover the following topics: Advanced AI and IoT Solutions for Environmental, Healthcare, and Security Challenges; Deep Learning Techniques for Wireless networks and Data Analysis.
The Definitive Guide to Power Query (M)
Learn how to use the Power Query M formula language and its functions effectively for better data modeling and impactful business intelligence reports.Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesGet comprehensive coverage of fundamental and advanced Power Query conceptsGain hands-on experience with practical examples based on real-world problemsDelve into the intricacies of Power Query M language for enhanced data manipulationBook DescriptionData transformation is a critical step in building data models and business intelligence reports. Power Query is an invaluable tool for anyone who wants to master data transformation, and this book will equip you with the knowledge and skills to make the most of it.The Definitive Guide to Power Query (M) will help you build a solid foundation in the Power Query M language. As you progress through the chapters, you'll learn how to use that knowledge to implement advanced concepts and data transformations before going on a no-compromise 'deep dive' into the Power Query M language.You'll also get to grips with optimizing performance, handling errors, and implementing efficient data processing techniques. As this is a hands-on guide, the practical examples in the chapters will help you gain the skills to apply Power Query to real-world problems and improve your data analysis capabilities.By the end of this book, you will be able to leverage all of Power Query's remarkable capabilities for data transformationWhat you will learnGain a strong understanding of Power query fundamentalsMaster various functions within Power Query to perform complex data operationsAcquire knowledge about values, types, and control structures in Power QueryDevelop proficiency in error handling techniquesLearn performance optimization strategies for Power QueryApply what you've learned to real-world scenarios, including common troublesome patternsWho this book is forThis book is for business analysts, business intelligence professionals, and power business users working with data who want to add Power Query mastery to their resume. This book will be beneficial for anyone who wants to automate their process of data cleaning and save huge amount of time. Having some basic experience in Power Query is recommended.Table of ContentsIntroducing MWorking with Power Query/MAccessing and Combining DataUnderstanding Values and ExpressionsUnderstanding Data TypesStructured ValuesConceptualizing MWorking with Nested StructuresParameters and Custom FunctionsDealing with Dates, Times, and DurationsComparers, Replacers, Combiners, and SplittersHandling Errors and DebuggingIteration and RecursionTroublesome Data PatternsOptimizing PerformanceEnabling Extensions
AI Innovation
"AI Innovation" demystifies artificial intelligence, guiding readers from foundational math to cutting-edge applications.Readers will learn: Core AI concepts: Machine learning, neural networks, deep learning.Practical skills: Data analysis, model training, ethical AI development.Real-world applications: AI in healthcare, cybersecurity, robotics, and more.Societal impact: By fostering AI literacy, the book empowers individuals to contribute to a future shaped by responsible and innovative AI solutions.
AI for Absolute Beginners
Curious about artificial intelligence but not sure where to start? Looking for an AI book you can actually understand?Now is an important time in history, and AI for Absolute Beginners is the perfect entry point to understand how AI works, what it can (and can't) do, and how it's shaping the future of business and society.Whether you're a student, professional, or lifelong learner, this book delivers a crystal-clear overview of today's most talked-about technologies-without the math, jargon, or programming required in most AI books. In fact, you'll be surprised at how quickly you can start thinking and speaking like an AI expert!What you'll learn: - The 3 phases of AI development and what's coming next...- The Missing Middle theory of leveraging what humans and AI do best- Generative AI and a preview of the Data Wars- Machine learning and a clear introduction on how it works- Deep learning and why it unlocks progress in all fields of AI- Natural language processing explained- Computer vision and why it's not as easy as you think...- Recommender systems and how to train them to your advantage- Privacy & ethical considerations you must know- The future of work and how to thrive in the new era of knowledge work- The new role of the Chief Intelligence Officer and how they are guiding AI transformation in organizationsWho is this book for?- Readers who want an AI for beginners book that actually makes sense- Professionals looking for a straightforward AI textbook book for business- Anyone who found "AI for Dummies book" too dense or dated- Entrepreneurs and decision-makers who need to understand AI at a strategic levelNo matter your background, AI for Absolute Beginners breaks down the fundamentals of artificial intelligence in a way that's smart, simple, and practical.Ready to get started? Buy a copy now-or send a free sample to your device to learn about AI!
Innovative IoT
"Innovative IoT: Comprehensive Guide" is an extensive exploration of the Internet of Things (IoT) and its transformative impact across various sectors. This guide is meticulously structured to provide a holistic understanding of IoT, from its foundational concepts to its advanced applications and future directions.The book begins with an Introduction to the Internet of Things (IoT), offering a historical perspective and defining the core principles of IoT. It then delves into the Enabling Technologies for IoT, explaining the critical technologies such as wireless communication, cloud storage, and edge computing that power IoT systems.The subsequent chapters explore the diverse applications of IoT across multiple domains: IoT Applications in Smart Cities: Examines how IoT is revolutionizing urban living through smart infrastructure and services.IoT in Industrial Automation: Discusses the role of IoT in enhancing industrial processes and efficiency.IoT in Agriculture: Highlights the precision farming techniques enabled by IoT sensors and devices.IoT in Healthcare: Explores the advancements in personalized patient care and medical services.IoT in Transportation: Covers the impact of IoT on transportation systems, including smart traffic management and autonomous vehicles.IoT in Retail: Analyzes how IoT is transforming the retail industry through smart inventory management and personalized shopping experiences.IoT in Energy Management: Focuses on sustainable energy solutions and smart grids.IoT in Environmental Monitoring: Discusses the use of IoT for monitoring and managing environmental conditions.IoT in Smart Homes: Explores the integration of IoT in home automation for enhanced comfort and convenience.IoT in Education: Examines the role of IoT in modernizing educational systems and learning environments.IoT in Finance: Discusses the impact of IoT on financial services and security.IoT in Entertainment: Highlights the innovations in entertainment and media driven by IoT.IoT in Tourism and Hospitality: Explores how IoT is enhancing the travel and hospitality experience.IoT in Manufacturing: Discusses the advancements in manufacturing processes through IoT.IoT in Logistics: Covers the improvements in supply chain management and logistics.IoT in Public Safety: Examines the role of IoT in enhancing public safety and emergency response.IoT in Sports: Highlights the use of IoT in sports analytics and performance enhancement.IoT in Real Estate: Discusses the impact of IoT on property management and smart buildings.IoT in Government and Public Services: Explores the applications of IoT in public administration and services.The book also addresses critical challenges and considerations: IoT Security and Privacy: Discusses the security and privacy concerns associated with IoT devices and networks.IoT Data Management and Analytics: Covers the strategies for managing and analyzing the vast amounts of data generated by IoT devices.IoT Standards and Protocols: Explains the importance of standardization and protocols in ensuring interoperability and reliability.Finally, the book looks ahead to the Future Directions and Challenges in IoT, providing insights into the potential advancements and obstacles in the IoT landscape."Innovative IoT: Comprehensive Guide" is not just a technical manual; it is a narrative that intertwines technology with the human experience. It envisions a future where IoT and artificial intelligence harmoniously coexist, driving innovation and enhancing the quality of life. This book serves as an invitation to explore the limitless possibilities of IoT and to envision a future where interconnected devices enrich every aspect of our lives.
Inevitable Knowledge
The Holy Grail of AI is artificial generative intelligence, a computer that can think human-like. However, human thinking is qualitatively more complex than computer calculations. So, the ultimate goal of AI cannot be achieved. Not quite. This book shows that a model of human-like, meaningful processing can be introduced based on a theory of cognition (how human processing can be abstracted in a series of events), semiotics (what signs are and what kind of distinctions can be communicated by signs), and computer science (how all this can be realized as a procedure). The emerging model offers a solution to the problem of artificial intelligence, not by itself, but in collaboration with the human agent by augmenting its intelligence. But there is more to it than that. Because of the fundamental nature of signs, the semiotic concept of meaning can be transformative for AI research. The book comprehensively covers several applications, including language processing, analyzing integrative negation processes, and solving mathematical problems. It delves into the intricate characteristics of the meaningful processing problem and the fascinating journey that led to its solution. The book provides insight into the historical background of the problem and the solution, enriching the reader's understanding and engagement. The text is self-contained. All necessary technical terms are explained.
An Integrated Approach to Modeling and Optimization in Engineering and Science
An Integrated Approach to Modeling and Optimization in Engineering and Science examines the effects of experimental design, mathematical modeling, and optimization processes for solving many different problems. The Experimental Design Method, Central Composite, Full Factorial, Taguchi, Box-Behnken, and D-Optimal methods are used, and the effects of the datasets obtained by these methods on mathematical modeling are investigated.This book will help graduates and senior undergraduates in courses on experimental design, modeling, optimization, and interdisciplinary engineering studies. It will also be of interest to research and development engineers and professionals working in scientific institutions based on design, modeling, and optimization.
Data Models for Banking, Finance, and Insurance
Jumpstart your financial application designs with proven conceptual and logical data models for banking, brokerage, and insurance.From the beginnings of Western and Eastern civilizations, data has always driven our banking, stock trading, and insurance contracts, yet this data was collected and stored in hand ledgers. With the advent of digital technology in the 20th century, financial firms expanded exponentially. The key to this expansion has been relational databases and, more recently, NoSQL, which enable us to store detailed information about customers and products. This book contains conceptual and logical data models for retail and commercial banking, including savings, checking, mortgages, and loans; credit cards, capital, and financial markets, including general ledger and chart of accounts; investment, brokerage and securities, including equities, options, and bonds; and insurance including property, casualty, and life. Also covered is how to translate into a physical design, including for analytics applications using star schemas.Whether you are a business specialist, analyst, regulator, or IT professional, this book provides a roadmap and set of patterns for the initial design and construction of fundamental data models for the most frequently used applications in the financial world.
Software Testing with Generative AI
Speed up your testing and deliver exceptional product quality with the power of AI tools. The more you test, the more you learn about your software. Software Testing with Generative AI shows you how you can expand, automate, and enhance your testing with Large Language Model (LLM)-based AI. Your team will soon be delivering higher quality tests, all in less time. In Software Testing with Generative AI you'll learn how to: - Spot opportunities to improve test quality with AI - Construct test automation with the support of AI tools - Formulate new ideas during exploratory testing using AI tools - Use AI tools to aid the design process of new features - Improve the testability of a context with the help of AI tools - Maximize your output with prompt engineering - Create custom LLMs for your business's specific needs Software Testing with Generative AI is full of hype-free advice for supporting your software testing with AI. In it, you'll find strategies from bestselling author Mark Winteringham to generate synthetic testing data, implement automation, and even augment and improve your test design with AI. Foreword by Nicola Martin. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology There's a simple rule in software testing: the more you test, the more you learn. And as any testing pro will tell you, good testing takes time. By integrating large language models (LLMs) and generative AI into your process, you can dramatically automate and enhance testing, improve quality and coverage, and deliver more meaningful results. About the book Software Testing with Generative AI shows you how AI can elevate every aspect of testing--automation, test data management, test scripting, exploratory testing, and more! Learn how to use AI coding tools like Copilot to guide test-driven development, get relevant feedback about your applications from ChatGPT, and use the OpenAI API to integrate AI into your data generation. You'll soon have higher-quality testing that takes up less of your time. What's inside - Improve test quality and coverage - AI-powered test automation - Build agents that act as testing assistants About the reader For developers, testers, and quality engineers. About the author Mark Winteringham is an experienced software tester who teaches many aspects of software testing. He is the author of Testing Web APIs. The technical editor on this book was Robert Walsh. Table of Contents Part 1 1 Enhancing testing with large language models 2 Large language models and prompt engineering 3 Artificial intelligence, automation, and testing Part 2 4 AI-assisted testing for developers 5 Test planning with AI support 6 Rapid data creation using AI 7 Accelerating and improving UI automation using AI 8 Assisting exploratory testing with artificial intelligence 9 AI agents as testing assistants Part 3 10 Introducing customized LLMs 11 Contextualizing prompts with retrieval-augmented generation 12 Fine-tuning LLMs with business domain knowledge Appendix A Setting up and using ChatGPT Appendix B Setting up and using GitHub Copilot Appendix C Exploratory testing notes
Machine Learning Algorithms
This book constitutes the refereed proceedings of the First International Conference on Machine Learning Algorithms, ICMLA 2024, held in Himachal Pradesh, India, during February 23-24, 2024. The 23 full papers and 17 short papers included in this book were carefully reviewed and selected from 400 submissions. They were organized in the following topical sections: machine learning; image processing; deep learning.
The Art of Problem Finding
About the Book -"The Art of Problem Finding - Discovering the Unknown Knowns & Unknown Unknowns" is the book that will serve its readers as a beacon for navigating the business world with innovation and strategic decision-making.Against the dynamic backdrop of the World Economic Forum's insights into the shifting skill sets that are required in the age of AI, this groundbreaking book pioneers a departure from the conventional path of problem-solving to explore the fertile grounds of problem finding. It is here, in the identification of unseen and unasked questions, the seeds of revolutionary innovations and strategies are sown.Historically, problem-solving has been the linchpin of progress since the dawn of the Industrial Revolution. However, the emergence of AI and advanced technologies has sparked a significant paradigm shift, compelling us to shift our focus from merely addressing existing challenges to proactively identifying and understanding new ones.This evolution underscores the book's core belief: in an age where AI reshapes every facet of our lives, the real goldmine lies not in solving known problems but in uncovering the unknown.Through an extensive array of real-world examples and detailed case studies, the book demonstrates the indispensable role of problem finding across diverse sectors such as technology, real estate, facilities management, gold & jewellery, automotive, and more.It not only showcases how this approach has led to groundbreaking innovations but also equips readers with a comprehensive set of frameworks and tools to cultivate a problem-finding mindset.Central to the book's narrative is the exploration of essential skills necessary for thriving in this new terrain such as critical thinking, curiosity, adaptability, cognitive flexibility, etc.
Data Engineering with AWS Cookbook
Master AWS data engineering services and techniques for orchestrating pipelines, building layers, and managing migrationsKey Features: - Get up to speed with the different AWS technologies for data engineering- Learn the different aspects and considerations of building data lakes, such as security, storage, and operations- Get hands on with key AWS services such as Glue, EMR, Redshift, QuickSight, and Athena for practical learning- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Performing data engineering with Amazon Web Services (AWS) combines AWS's scalable infrastructure with robust data processing tools, enabling efficient data pipelines and analytics workflows. This comprehensive guide to AWS data engineering will teach you all you need to know about data lake management, pipeline orchestration, and serving layer construction.Through clear explanations and hands-on exercises, you'll master essential AWS services such as Glue, EMR, Redshift, QuickSight, and Athena. Additionally, you'll explore various data platform topics such as data governance, data quality, DevOps, CI/CD, planning and performing data migration, and creating Infrastructure as Code. As you progress, you will gain insights into how to enrich your platform and use various AWS cloud services such as AWS EventBridge, AWS DataZone, and AWS SCT and DMS to solve data platform challenges.Each recipe in this book is tailored to a daily challenge that a data engineer team faces while building a cloud platform. By the end of this book, you will be well-versed in AWS data engineering and have gained proficiency in key AWS services and data processing techniques. You will develop the necessary skills to tackle large-scale data challenges with confidence.What You Will Learn: - Define your centralized data lake solution, and secure and operate it at scale- Identify the most suitable AWS solution for your specific needs- Build data pipelines using multiple ETL technologies- Discover how to handle data orchestration and governance- Explore how to build a high-performing data serving layer- Delve into DevOps and data quality best practices- Migrate your data from on-premises to AWSWho this book is for: If you're involved in designing, building, or overseeing data solutions on AWS, this book provides proven strategies for addressing challenges in large-scale data environments. Data engineers as well as big data professionals looking to enhance their understanding of AWS features for optimizing their workflow, even if they're new to the platform, will find value. Basic familiarity with AWS security (users and roles) and command shell is recommended. Table of Contents- Managing Data Lake Storage- Sharing Your Data Across Environments and Accounts- Ingesting and Transforming Your Data with AWS Glue- A Deep Dive into AWS Orchestration Frameworks- Running Big Data Workloads with Amazon EMR- Governing Your Platform- Data Quality Management- DevOps - Defining IaC and Building CI/CD Pipelines- Monitoring Data Lake Cloud Infrastructure- Building a Serving Layer with AWS Analytics Services- Migrating to AWS - Steps, Strategies, and Best Practices for Modernizing Your Analytics and Big Data Workloads- Harnessing the Power of AWS for Seamless Data Warehouse Migration- Strategizing Hadoop Migrations - Cost, Data, and Workflow Modernization with AWS
Hardware Description Language Demystified
Get familiar and work with the basic and advanced Modeling types in Verilog HDL Key FeaturesLearn about the step-wise process to use Verilog design tools such as Xilinx, Vivado, Cadence NC-SIM Explore the various types of HDL and its need Learn Verilog HDL modeling types using examples Learn advanced concept such as UDP, Switch level modeling Learn about FPGA based prototyping of the digital system Description Hardware Description Language (HDL) allows analysis and simulation of digital logic and circuits. The HDL is an integral part of the EDA (electronic design automation) tool for PLDs, microprocessors, and ASICs. So, HDL is used to describe a Digital System. The combinational and sequential logic circuits can be described easily using HDL. Verilog HDL, standardized as IEEE 1364, is a hardware description language used to model electronic systems. This book is a comprehensive guide about the digital system and its design using various VLSI design tools as well as Verilog HDL. The step-wise procedure to use various VLSI tools such as Xilinx, Vivado, Cadence NC-SIM, is covered in this book. It also explains the advanced concept such as User Define Primitives (UDP), switch level modeling, reconfigurable computing, etc. Finally, this book ends with FPGA based prototyping of the digital system. By the end of this book, you will understand everything related to digital system design. What will you learn Implement Adder, Subtractor, Adder-Cum-Subtractor using Verilog HDLExplore the various Modeling styles in Verilog HDL Implement Switch level modeling using Verilog HDL Get familiar with advanced modeling techniques in Verilog HDLGet to know more about FPGA based prototyping using Verilog HDL Who this book is for Anyone interested in Electronics and VLSI design and want to learn Digital System Design with Verilog HDL will find this book useful. IC developers can also use this book as a quick reference for Verilog HDL fundamentals & features. Table of Contents 1. An Introduction to VLSI Design Tools2. Need of Hardware Description Language (HDL)3. Logic Gate Implementation in Verilog HDL4. Adder-Subtractor Implementation Using Verilog HDL5. Multiplexer/Demultiplexer Implementation in Verilog HDL6. Encoder/Decoder Implementation Using Verilog HDL7. Magnitude Comparator Implementation Using Verilog HDL8. Flip-Flop Implementation Using Verilog HDL9. Shift Registers Implementation Using Verilog HDL10. Counter Implementation Using Verilog HDL11. Shift Register Counter Implementation Using Verilog HDL12. Advanced Modeling Techniques13. Switch Level Modeling14. FPGA Prototyping in Verilog HDLAbout the Author Dr. Cherry Bhargava is working as an associate professor and head, VLSI domain, School of Electrical and Electronics Engineering at Lovely Professional University, Punjab, India. She has more than 14 years of teaching and research experience. She is Ph.D. (ECE), IKGPTU, M.Tech (VLSI Design & CAD) Thapar University and B.Tech (Electronics and Instrumentation) from Kurukshetra University. She is GATE qualified with All India Rank 428. She has authored about 50 technical research papers in SCI, Scopus indexed quality journals, and national/international conferences. Dr. Rajkumar Sarma received his B.E. in Electronics and Communications Engineering from Vinayaka Mission's University, Salem, India & M.Tech degree from Lovely Professional University, Phagwara, Punjab and currently pursuing Ph.D. from Lovely Professional University, Phagwara, Punjab.
Artificial Intelligence
Discover the world of artificial intelligence with this comprehensive guide. From its evolution to its impact on industries, this book covers everything you need to know. Learn about data analysis, machine learning, natural language processing, computer vision, robotics, AI in business, ethics, and the future of AI. Packed with insights and practical examples, this book is a must-read for anyone interested in the exciting field of AI.
Data Analytics for Intelligent Transportation Systems
Data Analytics for Intelligent Transportation Systems, Second Edition provides in-depth coverage of data-enabled methods for analyzing intelligent transportation systems (ITS), including the tools needed to implement these methods using big data analytics and other computing techniques. The book examines the major characteristics of connected transportation systems, along with the fundamental concepts of how to analyze the data they produce. It explores collecting, archiving, processing, and distributing the data, designing data infrastructures, data management and delivery systems, and the required hardware and software technologies. Other sections provide extensive coverage of existing and forthcoming intelligent transportation systems and data analytics technologies. All fundamentals/concepts presented in this book are explained in the context of ITS. Users will learn everything from the basics of different ITS data types and characteristics to how to evaluate alternative data analytics for different ITS applications. In addition, they will discover how to design effective data visualizations, tactics on the planning process, and how to evaluate alternative data analytics for different connected transportation applications, along with key safety and environmental applications for both commercial and passenger vehicles, data privacy and security issues, and the role of social media data in traffic planning.
Learn Generative AI with Pytorch
Learn how generative AI works by building your very own models that can write coherent text, create realistic images, and even make lifelike music. Learn Generative AI with PyTorch teaches the underlying mechanics of generative AI by building working AI models from scratch. Throughout, you'll use the intuitive PyTorch framework that's instantly familiar to anyone who's worked with Python data tools. Along the way, you'll master the fundamentals of General Adversarial Networks (GANs), Transformers, Large Language Models (LLMs), variational autoencoders, diffusion models, LangChain, and more! In Learn Generative AI with PyTorch you'll build these amazing models: - A simple English-to-French translator - A text-generating model as powerful as GPT-2 - A diffusion model that produces realistic flower images - Music generators using GANs and Transformers - An image style transfer model - A zero-shot know-it-all agent The generative AI projects you create use the same underlying techniques and technologies as full-scale models like GPT-4 and Stable Diffusion. You don't need to be a machine learning expert--you can get started with just some basic Python programming skills. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Transformers, Generative Adversarial Networks (GANs), diffusion models, LLMs, and other powerful deep learning patterns have radically changed the way we manipulate text, images, and sound. Generative AI may seem like magic at first, but with a little Python, the PyTorch framework, and some practice, you can build interesting and useful models that will train and run on your laptop. This book shows you how. About the book Learn Generative AI with PyTorch introduces the underlying mechanics of generative AI by helping you build your own working AI models. You'll begin by creating simple images using a GAN, and then progress to writing a language translation transformer line-by-line. As you work through the fun and fascinating projects, you'll train models to create anime images, write like Hemingway, make music like Mozart, and more. You just need Python and a few machine learning basics to get started. You'll learn the rest as you go! What's inside - Build an English-to-French translator - Create a text-generation LLM - Train a diffusion model to produce high-resolution images - Music generators using GANs and Transformers About the reader Examples use simple Python. No deep learning experience required. About the author Mark Liu is the founding director of the Master of Science in Finance program at the University of Kentucky. The technical editor on this book was Emmanuel Maggiori. Table of Contents Part 1 1 What is generative AI and why PyTorch? 2 Deep learning with PyTorch 3 Generative adversarial networks: Shape and number generation Part 2 4 Image generation with generative adversarial networks 5 Selecting characteristics in generated images 6 CycleGAN: Converting blond hair to black hair 7 Image generation with variational autoencoders Part 3 8 Text generation with recurrent neural networks 9 A line-by-line implementation of attention and Transformer 10 Training a Transformer to translate English to French 11 Building a generative pretrained Transformer from scratch 12 Training a Transformer to generate text Part 4 13 Music generation with MuseGAN 14 Building and training a music Transformer 15 Diffusion models and text-to-image Transformers 16 Pretrained large language models and the LangChain library Appendixes A Installing Python, Jupyter Notebook, and PyTorch B Minimally qualified readers and deep learning basics
Apache Spark for Machine Learning
Develop your data science skills with Apache Spark to solve real-world problems for Fortune 500 companies using scalable algorithms on large cloud computing clustersKey Features: - Apply techniques to analyze big data and uncover valuable insights for machine learning- Learn to use cloud computing clusters for training machine learning models on large datasets- Discover practical strategies to overcome challenges in model training, deployment, and optimization- Purchase of the print or Kindle book includes a free PDF eBookBook Description: 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.What You Will Learn: - Master Apache Spark for efficient, large-scale data processing and analysis- Understand core machine learning concepts and their applications with Spark- Implement data preprocessing techniques for feature extraction and transformation- Explore supervised learning methods - regression and classification algorithms- Apply unsupervised learning for clustering tasks and recommendation systems- Discover frequent pattern mining techniques to uncover data trendsWho this book is for: This book is ideal for data scientists, ML engineers, data engineers, students, and researchers who want to deepen their knowledge of Apache Spark's tools and algorithms. It's a must-have for those struggling to scale models for real-world problems and a valuable resource for preparing for interviews at Fortune 500 companies, focusing on large dataset analysis, model training, and deployment.Table of Contents- An Overview of Machine Learning Concepts- Data Processing with Spark- Feature Extraction and Transformation- Building a Regression System- Building a Classification System- Building a Clustering System- Building a Recommendation System- Mining Frequent Patterns- Deploying a Model
Snowflake Data Engineering
A practical introduction to data engineering on the powerful Snowflake cloud data platform. Data engineers create the pipelines that ingest raw data, transform it, and funnel it to the analysts and professionals who need it. The Snowflake cloud data platform provides a suite of productivity-focused tools and features that simplify building and maintaining data pipelines. In Snowflake Data Engineering, Snowflake Data Superhero Maja Ferle shows you how to get started. In Snowflake Data Engineering you will learn how to: - Ingest data into Snowflake from both cloud and local file systems - Transform data using functions, stored procedures, and SQL - Orchestrate data pipelines with streams and tasks, and monitor their execution - Use Snowpark to run Python code in your pipelines - Deploy Snowflake objects and code using continuous integration principles - Optimize performance and costs when ingesting data into Snowflake Snowflake Data Engineering reveals how Snowflake makes it easy to work with unstructured data, set up continuous ingestion with Snowpipe, and keep your data safe and secure with best-in-class data governance features. Along the way, you'll practice the most important data engineering tasks as you work through relevant hands-on examples. Throughout, author Maja Ferle shares design tips drawn from her years of experience to ensure your pipeline follows the best practices of software engineering, security, and data governance. Foreword by Joe Reis. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Pipelines that ingest and transform raw data are the lifeblood of business analytics, and data engineers rely on Snowflake to help them deliver those pipelines efficiently. Snowflake is a full-service cloud-based platform that handles everything from near-infinite storage, fast elastic compute services, inbuilt AI/ML capabilities like vector search, text-to-SQL, code generation, and more. This book gives you what you need to create effective data pipelines on the Snowflake platform. About the book Snowflake Data Engineering guides you skill-by-skill through accomplishing on-the-job data engineering tasks using Snowflake. You'll start by building your first simple pipeline and then expand it by adding increasingly powerful features, including data governance and security, adding CI/CD into your pipelines, and even augmenting data with generative AI. You'll be amazed how far you can go in just a few short chapters! What's inside - Ingest data from the cloud, APIs, or Snowflake Marketplace - Orchestrate data pipelines with streams and tasks - Optimize performance and cost About the reader For software developers and data analysts. Readers should know the basics of SQL and the Cloud. About the author Maja Ferle is a Snowflake Subject Matter Expert and a Snowflake Data Superhero who holds the SnowPro Advanced Data Engineer and the SnowPro Advanced Data Analyst certifications. Table of Contents Part 1 1 Data engineering with Snowflake 2 Creating your first data pipeline Part 2 3 Best practices for data staging 4 Transforming data 5 Continuous data ingestion 6 Executing code natively with Snowpark 7 Augmenting data with outputs from large language models 8 Optimizing query performance 9 Controlling costs 10 Data governance and access control Part 3 11 Designing data pipelines 12 Ingesting data incrementally 13 Orchestrating data pipelines 14 Testing for data integrity and completeness 15 Data pipeline continuous integration
AI Wealth Entrepreneur
Discover how you can build solid passive income streams in just 30 minutes a day with Artificial Intelligence, even if you're a complete beginner! Whether you're a parent managing a busy professional life or an entrepreneur seeking new AI income tools, this comprehensive guide is designed to help you succeed in the AI-driven economy.Unlock the secrets to financial freedom with AI Wealth Entrepreneur, your ultimate guide to harnessing the power of AI for passive income. Inside you'll find a complete step-by-step blueprint for putting AI to work in your business to: Immediately get back at least one day a week of productivityKnow what tools to use, what tools do what, and how to "stack" systems for better resultsEmbrace AI culture in your business instead of fearing itAI Prompts Database: A vast library of prompts to ensure you get the best out of AI platformsAccess a list of 100+ Complete AI Tools Directory: A comprehensive directory of essential AI tools, resources, references, and software to create music, photos, games, and books!
Computation of Artificial Intelligence and Machine Learning
The two-volume set, CCIS 2184-2185, constitutes the refereed proceedings of the First International Conference on Computation of Artificial Intelligence and Machine Learning, ICCAIML 2024, held in Jaipur, India, in January 18-19, 2024. The 60 papers included in these volumes were carefully reviewed and selected from 645 submissions. These papers focus on various subject areas within the field of Artificial Intelligence and Machine Learning, such as Neural Networks and Deep Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, Data Mining and Big Data Analytics, AI in Healthcare and Biomedical Applications, Autonomous Systems and Robotics, AI Ethics and Fairness, AI in Finance and Eco-nomics.
AI Mastery for Finance Professionals
Are you ready to harness the transformative power of AI in finance?AI Mastery for Finance Professionals is the definitive guide for navigating the AI revolution that is reshaping the financial landscape. This book demystifies complex concepts and equips you with the knowledge and tools to leverage AI for smarter decision-making, improved risk management, and unparalleled innovation. As AI removes traditional constraints on scale, scope, and learning, it is redefining how financial institutions create, capture, share, and deliver value. With AI-driven processes enabling unprecedented efficiency, accuracy, and sophistication, the rules of the game are changing. This book is your essential guide to understanding and capitalizing on this transformative technology.Unlock the power of AI in finance with: Deep dives into machine learning, deep learning, and cutting-edge AI techniques tailored for financeReal-world applications across trading, investment management, fraud detection, customer service, lending, compliance, and morePractical insights on developing and implementing an AI strategy, building capabilities, measuring value, and managing risksIlluminating case studies, examples, and actionable advice for driving competitive advantage in the age of AIDon't get left behind in the AI-powered future of finance. Arm yourself with the knowledge and tools to harness the potential of AI, navigate disruption, and lead the way forward. Whether you're a finance leader, professional, or student, this book is your roadmap to success in the era of intelligent technologies. With a foreword by Brian Chess, Senior Vice President, Technology and AI, Oracle NetSuite Global Business Unit, AI Mastery for Finance Professionals is your key to transforming your career and organization in the AI-powered future of finance.
AI Mastery for Finance Professionals
Are you ready to harness the transformative power of AI in finance?AI Mastery for Finance Professionals is the definitive guide for navigating the AI revolution that is reshaping the financial landscape. This book demystifies complex concepts and equips you with the knowledge and tools to leverage AI for smarter decision-making, improved risk management, and unparalleled innovation. As AI removes traditional constraints on scale, scope, and learning, it is redefining how financial institutions create, capture, share, and deliver value. With AI-driven processes enabling unprecedented efficiency, accuracy, and sophistication, the rules of the game are changing. This book is your essential guide to understanding and capitalizing on this transformative technology.Unlock the power of AI in finance with: Deep dives into machine learning, deep learning, and cutting-edge AI techniques tailored for financeReal-world applications across trading, investment management, fraud detection, customer service, lending, compliance, and morePractical insights on developing and implementing an AI strategy, building capabilities, measuring value, and managing risksIlluminating case studies, examples, and actionable advice for driving competitive advantage in the age of AIDon't get left behind in the AI-powered future of finance. Arm yourself with the knowledge and tools to harness the potential of AI, navigate disruption, and lead the way forward. Whether you're a finance leader, professional, or student, this book is your roadmap to success in the era of intelligent technologies. With a foreword by Brian Chess, Senior Vice President, Technology and AI, Oracle NetSuite Global Business Unit, AI Mastery for Finance Professionals is your key to transforming your career and organization in the AI-powered future of finance.
Neo4j: The Definitive Guide
Looking to improve the performance of Cypher queries or learn how to model graphs to support business use cases? A graph database like Neo4j can help. In fact, many enterprises are leveraging Neo4j to power their business-critical applications. This book offers practical and concise recipes on how and when to successfully leverage Neo4j into architectures. Authors Christophe Willemsen and Luanne Misquitta walk you through typical Neo4j implementation strategies from proof of concept to iterative improvements and, finally, to production readiness and beyond. By the end of this book, you should understand how to: Make practical decisions in the proof of concept stage to maximize value Revisit and revise your decisions when transitioning to production Configure and implement observability features for in-production data graphs Integrate graph databases into existing enterprise architectures
Mathematical Modelling
Title: The Badass Guide to Mathematical Modelling: Embrace Math, Solve Problems, and Unleash Your Inner Genius Title: The Badass Guide to Mathematical Modelling: Embrace Math, Solve Problems, and Unleash Your Inner Genius
Run Your Own Mail Server
You Against the Email EmpireMessage services appear and disappear, but email remains. One of the Internet's oldest and most open protocols, email reaches everywhere. Dominated by a handful of carriers, yet still manageable by the rest of us. If you do it right.Setting up the email server is the easy part. The protocols that support email? Those are hard. SPF. DKIM. DMARC. BIMI and MTA-TLS and TLS-RPT. DNS standards that apply to nothing else on the modern Internet. Block lists. Graylisting.Email is a protocol unlike any other, yet among our most essential.Never surrender the protocols.Reclaim your connections.Run your own mail server.
Artificial Intelligence in Mechanical and Industrial Engineering
Artificial Intelligence in Mechanical and Industrial Engineering offers a unified platform for the dissemination of basic and applied knowledge on the integration of artificial intelligence within the realm of mechanical and industrial engineering. The book covers the tools and information needed to build successful careers and a source of knowledge for those working with AI within these domains.The book offers a systematic approach to explicate fundamentals as well as recent advances. It incorporates various case studies for major topics as well as numerous examples. It will also include real-time intelligent automation and associated supporting methodologies and techniques, and cover decision-support systems, as well as applications of Chaos Theory and Fractals.The book will give scientists, researchers, instructors, students, and practitioners the tools and information needed to build successful careers and to be an impetus to advancements in next-generation mechanical and industrial engineering domains.
Learn Ai-Assisted Python Programming, Second Edition
See how an AI assistant can bring your ideas to life immediately! Once, to be a programmer you had to write every line of code yourself. Now tools like GitHub Copilot can instantly generate working programs based on your description in plain English. An instant bestseller, Learn AI-Assisted Python Programming has taught thousands of aspiring programmers how to write Python the easy way--with the help of AI. It's perfect for beginners, or anyone who's struggled with the steep learning curve of traditional programming. In Learn AI-Assisted Python Programming, Second Edition you'll learn how to: - Write fun and useful Python applications--no programming experience required! - Use the GitHub Copilot AI coding assistant to create Python programs - Write prompts that tell Copilot exactly what to do - Read Python code and understand what it does - Test your programs to make sure they work the way you want them to - Fix code with prompt engineering or human tweaks - Apply Python creatively to help out on the job AI moves fast, and so the new edition of Learn AI-Assisted Python Programming, Second Edition is fully updated to take advantage of the latest models and AI coding tools. Written by two esteemed computer science university professors, it teaches you everything you need to start programming Python in an AI-first world. You'll learn skills you can use to create working apps for data analysis, automating tedious tasks, and even video games. Plus, in this new edition, you'll find groundbreaking techniques for breaking down big software projects into smaller tasks AI can easily achieve. Foreword by Beth Simon. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology The way people write computer programs has changed forever. Using GitHub Copilot, you describe in plain English what you want your program to do, and the AI generates it instantly. About the book This book shows you how to create and improve Python programs using AI--even if you've never written a line of computer code before. Spend less time on the slow, low-level programming details and instead learn how an AI assistant can bring your ideas to life immediately. As you go, you'll even learn enough of the Python language to understand and improve what your AI assistant creates. What's inside - Prompts for working code - Tweak code manually and with AI help - AI-test your programs - Let AI handle tedious details About the reader If you can move files around on your computer and install new programs, you can learn to write useful software! About the author Dr. Leo Porter is a Teaching Professor at UC San Diego. Dr. Daniel Zingaro is an Associate Teaching Professor at the University of Toronto. The technical editor on this book was Peter Morgan. Table of Contents 1 Introducing AI-assisted programming with GitHub Copilot 2 Getting started with Copilot 3 Designing functions 4 Reading Python code: Part 1 5 Reading Python code: Part 2 6 Testing and prompt engineering 7 Problem decomposition 8 Debugging and better understanding your code 9 Automating tedious tasks 10 Making some games 11 Creating an authorship identification program 12 Future directions
Generative AI in Action
Generative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action! Generative AI in Action is the comprehensive and concrete guide to generative AI you've been searching for. It introduces both AI's fundamental principles and its practical applications in an enterprise context--from generating text and images for product catalogs and marketing campaigns, to technical reporting, and even writing software. Inside, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. Inside Generative AI in Action you will find: - A practical overview of of generative AI applications - Architectural patterns, integration guidance, and best practices for generative AI - The latest techniques like RAG, prompt engineering, and multi-modality - The challenges and risks of generative AI like hallucinations and jailbreaks - How to integrate generative AI into your business and IT strategy Generative AI in Action is full of real-world use cases for generative AI, showing you where and how to start integrating this powerful technology into your products and workflows. You'll benefit from tried-and-tested implementation advice, as well as application architectures to deploy GenAI in production at enterprise scale. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology In controlled environments, deep learning systems routinely surpass humans in reading comprehension, image recognition, and language understanding. Large Language Models (LLMs) can deliver similar results in text and image generation and predictive reasoning. Outside the lab, though, generative AI can both impress and fail spectacularly. So how do you get the results you want? Keep reading! About the book Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you'll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You'll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems. What's inside - Best practices for deploying Generative AI apps - Production-quality RAG - Adapting GenAI models to your specific domain About the reader For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI. About the author Amit Bahree is Principal Group Product Manager for the Azure AI engineering team at Microsoft. The technical editor on this book was Wee Hyong Tok. Table of Contents Part 1 1 Introduction to generative AI 2 Introduction to large language models 3 Working through an API: Generating text 4 From pixels to pictures: Generating images 5 What else can AI generate? Part 2 6 Guide to prompt engineering 7 Retrieval-augmented generation: The secret weapon 8 Chatting with your data 9 Tailoring models with model adaptation and fine-tuning Part 3 10 Application architecture for generative AI apps 11 Scaling up: Best practices for production deployment 12 Evaluations and benchmarks 13 Guide to ethical GenAI: Principles, practices, and pitfalls A The book's GitHub repository B Responsible AI tools
Build a Large Language Model (from Scratch)
Learn how to create, train, and tweak large language models (LLMs) by building one from the ground up! In Build a Large Language Model (from Scratch) bestselling author Sebastian Raschka guides you step by step through creating your own LLM. Each stage is explained with clear text, diagrams, and examples. You'll go from the initial design and creation, to pretraining on a general corpus, and on to fine-tuning for specific tasks. Build a Large Language Model (from Scratch) teaches you how to: - Plan and code all the parts of an LLM - Prepare a dataset suitable for LLM training - Fine-tune LLMs for text classification and with your own data - Use human feedback to ensure your LLM follows instructions - Load pretrained weights into an LLM Build a Large Language Model (from Scratch) takes you inside the AI black box to tinker with the internal systems that power generative AI. As you work through each key stage of LLM creation, you'll develop an in-depth understanding of how LLMs work, their limitations, and their customization methods. Your LLM can be developed on an ordinary laptop, and used as your own personal assistant. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Physicist Richard P. Feynman reportedly said, "I don't understand anything I can't build." Based on this same powerful principle, bestselling author Sebastian Raschka guides you step by step as you build a GPT-style LLM that you can run on your laptop. This is an engaging book that covers each stage of the process, from planning and coding to training and fine-tuning. About the book Build a Large Language Model (From Scratch) is a practical and eminently-satisfying hands-on journey into the foundations of generative AI. Without relying on any existing LLM libraries, you'll code a base model, evolve it into a text classifier, and ultimately create a chatbot that can follow your conversational instructions. And you'll really understand it because you built it yourself! What's inside - Plan and code an LLM comparable to GPT-2 - Load pretrained weights - Construct a complete training pipeline - Fine-tune your LLM for text classification - Develop LLMs that follow human instructions About the reader Readers need intermediate Python skills and some knowledge of machine learning. The LLM you create will run on any modern laptop and can optionally utilize GPUs. About the author Sebastian Raschka is a Staff Research Engineer at Lightning AI, where he works on LLM research and develops open-source software. The technical editor on this book was David Caswell. Table of Contents 1 Understanding large language models 2 Working with text data 3 Coding attention mechanisms 4 Implementing a GPT model from scratch to generate text 5 Pretraining on unlabeled data 6 Fine-tuning for classification 7 Fine-tuning to follow instructions A Introduction to PyTorch B References and further reading C Exercise solutions D Adding bells and whistles to the training loop E Parameter-efficient fine-tuning with LoRA
Build Custom GPTs for the GPT Store
In this book, we take you on a fun, hands-on and pragmatic journey to learning how to build your own custom GPTs. You'll start building your first GPT within minutes. Every section is written in a bite-sized manner and straight to the point as I don't want to waste your time (and most certainly mine) on the content you don't need. In the end, you will have the skills to create a custom GPT for the GPT Store.In the course of this book, we will cover: Chapter 1: Introduction to GPTsChapter 2: Customizing your GPTChapter 3: Enchancing Instructions for GPT PerformanceChapter 4: Distributing your Custom GPT and Setting a Builder ProfileChapter 5: Exploring GPT CapabilitiesChapter 6: Working with DocumentsChapter 7: Integrating APIs into your Custom GPTsChapter 8: Linking a GPT with Zapier AI ActionsThe goal of this book is to teach you to build custom GPTs in a manageable way without overwhelming you. We focus only on the essentials and cover the material in a hands-on practice manner for you to code along.
Python Data Cleaning and Preparation Best Practices
Take your data preparation skills to the next level by converting any type of data asset into a structured, formatted, and readily usable datasetKey Features: - Maximize the value of your data through effective data cleaning methods- Enhance your data skills using strategies for handling structured and unstructured data- Elevate the quality of your data products by testing and validating your data pipelines- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Professionals face several challenges in effectively leveraging data in today's data-driven world. One of the main challenges is the low quality of data products, often caused by inaccurate, incomplete, or inconsistent data. Another significant challenge is the lack of skills among data professionals to analyze unstructured data, leading to valuable insights being missed that are difficult or impossible to obtain from structured data alone.To help you tackle these challenges, this book will take you on a journey through the upstream data pipeline, which includes the ingestion of data from various sources, the validation and profiling of data for high-quality end tables, and writing data to different sinks. You'll focus on structured data by performing essential tasks, such as cleaning and encoding datasets and handling missing values and outliers, before learning how to manipulate unstructured data with simple techniques. You'll also be introduced to a variety of natural language processing techniques, from tokenization to vector models, as well as techniques to structure images, videos, and audio.By the end of this book, you'll be proficient in data cleaning and preparation techniques for both structured and unstructured data.What You Will Learn: - Ingest data from different sources and write it to the required sinks- Profile and validate data pipelines for better quality control- Get up to speed with grouping, merging, and joining structured data- Handle missing values and outliers in structured datasets- Implement techniques to manipulate and transform time series data- Apply structure to text, image, voice, and other unstructured dataWho this book is for: Whether you're a data analyst, data engineer, data scientist, or a data professional responsible for data preparation and cleaning, this book is for you. Working knowledge of Python programming is needed to get the most out of this book. Table of Contents- Data Ingestion Techniques- Importance of Data Quality- Data Profiling - Understanding Data Structure, Quality, and Distribution- Cleaning Messy Data and Data Manipulation- Data Transformation - Merging and Concatenating- Data Grouping, Aggregation, Filtering, and Applying Functions- Data Sinks- Detecting and Handling Missing Values and Outliers- Normalization and Standardization- Handling Categorical Features- Consuming Time Series Data- Text Preprocessing in the Era of LLMs- Image and Audio Preprocessing with LLMs
Run Your Own Mail Server
You Against the Email EmpireMessage services appear and disappear, but email remains. One of the Internet's oldest and most open protocols, email reaches everywhere. Dominated by a handful of carriers, yet still manageable by the rest of us. If you do it right.Setting up the email server is the easy part. The protocols that support email? Those are hard. SPF. DKIM. DMARC. BIMI and MTA-TLS and TLS-RPT. DNS standards that apply to nothing else on the modern Internet. Block lists. Graylisting.Email is a protocol unlike any other, yet among our most essential.Never surrender the protocols.Reclaim your connections.Run your own mail server.
Claude 3 & 3.5 Crash Course
In this book, we take you on a fun, hands-on and pragmatic journey to learning how to use Claude 3.5 for business applications and build apps with the Claude API. You'll learn how to use Claude features like Artifacts and Projects within minutes. Every section is written in a bite-sized manner and straight to the point as I don't want to waste your time (and most certainly mine) on the content you don't need.In the course of this book, we will cover: Chapter 1: Introduction to Claude 3 and 3.5Chapter 2: Claude UI, Applications and FeaturesChapter 3: Claude ArtifactsChapter 4: Introduction to Claude ProjectsChapter 5: Enchancing Instructions for Project PerformanceChapter 6: Introduction to Claude APIThe goal of this book is to teach you to use Claude in a practical way without overwhelming you. We focus only on the essentials and cover the material in a hands-on practice manner for you to follow along.
LangChain Crash Course
In this short course, we take you on a fun, hands-on and pragmatic journey to learn how to build LLM powered apps using LangChain. You'll start building your first Generative AI app within minutes. Every section is recorded in a bite-sized manner and straight to the point as I don't want to waste your time (and most certainly mine) on the content you don't need.The goal of this course is to teach you LangChain development in a manageable way without overwhelming you. We focus only on the essentials and cover the material in a hands-on practice manner for you to code along.Working Through This CourseThis course is purposely broken down into short sections where the development process of each section will center on different essential topics. The course a practical hands on approach to learning through practice. You learn best when you code along with the examples.
Marketing with AI for Dummies
Stay ahead in the marketing game by harnessing the power of artificial intelligence Marketing with AI For Dummies is your introduction to the revolution that's occurring in the marketing industry, thanks to artificial intelligence tools that can create text, images, audio, video, websites, and beyond. This book captures the insight of leading marketing executive Shiv Singh on how AI will change marketing, helping new and experienced marketers tackle AI marketing plans, content, creative assets, and localized campaigns. You'll also learn to manage SEO and customer personalization with powerful new technologies. Peek at the inner workings of AI marketing tools to see how you can best leverage their capabilities Identify customers, create content, customize outreach, and personalize customer experience with AI Consider how your team, department, or organization can be retooled to thrive in an AI-enabled world Learn from valuable case studies that show how large organizations are using AI in their campaigns This easy-to-understand Dummies guide is perfect for marketers at all levels, as well as those who only wear a marketing hat occasionally. Whatever your professional background, Marketing with AI For Dummies will usher you into the future of marketing.
Object-Role Modeling Fundamentals
Object-Role Modeling (ORM) is a fact-based approach to data modeling that expresses the information requirements of any business domain simply in terms of objects that play roles in relationships. All facts of interest are treated as instances of attribute-free structures known as fact types where the relationship may be unary (e.g. Person smokes) binary (e.g. Person was born on Date) ternary (e.g. Customer bought Product on Date) or longer. Fact types facilitate natural expression are easy to populate with examples for validation purposes and have greater semantic stability than attribute-based structures such as those used in Entity Relationship Modeling (ER) or the Unified Modeling Language (UML).All relevant facts constraints and derivation rules are expressed in controlled natural language sentences that are intelligible to users in the business domain being modeled. This allows ORM data models to be validated by business domain experts who are unfamiliar with ORM's graphical notation. For the data modeler ORM's graphical notation covers a much wider range of constraints than can be expressed in industrial ER or UML class diagrams and thus allows rich visualization of the underlying semantics.Suitable for both novices and experienced practitioners this book covers the fundamentals of the ORM approach. Written in easy-to-understand language it shows how to design an ORM model illustrating each step with simple examples. Each chapter ends with a practical lab that discusses how to use the freeware NORMA tool to enter ORM models and use it to automatically generate verbalizations of the model and map it to a relational database.
UML and Data Modeling
Here you will learn how to develop an attractive easily readable conceptual business-oriented entity/relationship model using a variation on the UML Class Model notation. This book has two audiences: Data modelers (both analysts and database designers) who are convinced that UML has nothing to do with them; and UML experts who don't realize that architectural data modeling really is different from object modeling (and that the differences are important).David Hay's objective is to finally bring these two groups together in peace. Here all modelers will receive guidance on how to produce a high quality (that is readable) entity/relationship model to describe the data architecture of an organization. The notation involved happens to be the one for class models in the Unified Modeling Language even though UML was originally developed to support object-oriented design. Designers have a different view of the world from those who develop business-oriented conceptual data models which means that to use UML for architectural modeling requires some adjustments. These adjustments are described in this book. David Hay is the author of Enterprise Model Patterns: Describing the World a comprehensive model of a generic enterprise. The diagrams were at various levels of abstraction and they were all rendered in the slightly modified version of UML Class Diagrams presented here. This book is a handbook to describe how to build models such as these. By way of background an appendix provides a history of the two groups revealing the sources of their different attitudes towards the system development process.
Principles of Verilog Digital Design
Covering both the fundamentals and the in-depth topics related to Verilog Digital Design, both students and experts can benefit from reading this book by gaining a comprehensive understanding of how modern electronic products are designed and implemented.
Data Quality in the Age of AI
Unlock the power of data with expert insights to enhance data quality, maximizing the potential of AI, and establishing a data-centric cultureKey Features: - Gain a profound understanding of the interplay between data quality and AI- Explore strategies to improve data quality with practical implementation and real-world results- Acquire the skills to measure and evaluate data quality, empowering data-driven decisions- Purchase of the print or Kindle book includes a free PDF eBookBook Description: As organizations worldwide seek to revamp their data strategies to leverage AI advancements and benefit from newfound capabilities, data quality emerges as the cornerstone for success. Without high-quality data, even the most advanced AI models falter. Enter Data Quality in the Age of AI, a detailed report that illuminates the crucial role of data quality in shaping effective data strategies.Packed with actionable insights, this report highlights the critical role of data quality in your overall data strategy. It equips teams and organizations with the knowledge and tools to thrive in the evolving AI landscape, serving as a roadmap for harnessing the power of data quality, enabling them to unlock their data's full potential, leading to improved performance, reduced costs, increased revenue, and informed strategic decisions.What You Will Learn: - Discover actionable steps to establish data quality as the foundation of your data culture- Enhance data quality directly at its source with effective strategies and best practices- Elevate data quality standards and enhance data literacy within your organization- Identify and measure data quality within the dataset- Adopt a product mindset to address data quality challenges- Explore emerging architectural patterns like data mesh and data contracts- Assign roles, responsibilities, and incentives for data generators- Gain insights from real-world case studiesWho this book is for: This report is for data leaders and decision-makers, including CTOs, CIOs, CISOs, CPOs, and CEOs responsible for shaping their organization's data strategy to maximize data value, especially those interested in harnessing recent AI advancements.Table of Contents- Executive Summary - Introduction: Understanding data quality- Unlocking AI's potential with Data- Improving data quality at the source- Case studies: Positive impact of data quality- Cultivating a data culture that values quality- Conclusion: Embracing a quality-driven data culture
In-Memory Analytics with Apache Arrow - Second Edition
Harness the power of Apache Arrow to optimize tabular data processing and develop robust, high-performance data systems with its standardized, language-independent columnar memory formatKey Features: - Explore Apache Arrow's data types and integration with pandas, Polars, and Parquet- Work with Arrow libraries such as Flight SQL, Acero compute engine, and Dataset APIs for tabular data- Enhance and accelerate machine learning data pipelines using Apache Arrow and its subprojects- Purchase of the print or Kindle book includes a free PDF eBookBook Description: Apache Arrow is an open source, columnar in-memory data format designed for efficient data processing and analytics. This book harnesses the author's 15 years of experience to show you a standardized way to work with tabular data across various programming languages and environments, enabling high-performance data processing and exchange.This updated second edition gives you an overview of the Arrow format, highlighting its versatility and benefits through real-world use cases. It guides you through enhancing data science workflows, optimizing performance with Apache Parquet and Spark, and ensuring seamless data translation. You'll explore data interchange and storage formats, and Arrow's relationships with Parquet, Protocol Buffers, FlatBuffers, JSON, and CSV. You'll also discover Apache Arrow subprojects, including Flight, SQL, Database Connectivity, and nanoarrow. You'll learn to streamline machine learning workflows, use Arrow Dataset APIs, and integrate with popular analytical data systems such as Snowflake, Dremio, and DuckDB. The latter chapters provide real-world examples and case studies of products powered by Apache Arrow, providing practical insights into its applications.By the end of this book, you'll have all the building blocks to create efficient and powerful analytical services and utilities with Apache Arrow.What You Will Learn: - Use Apache Arrow libraries to access data files, both locally and in the cloud- Understand the zero-copy elements of the Apache Arrow format- Improve the read performance of data pipelines by memory-mapping Arrow files- Produce and consume Apache Arrow data efficiently by sharing memory with the C API- Leverage the Arrow compute engine, Acero, to perform complex operations- Create Arrow Flight servers and clients for transferring data quickly- Build the Arrow libraries locally and contribute to the communityWho this book is for: This book is for developers, data engineers, and data scientists looking to explore the capabilities of Apache Arrow from the ground up. Whether you're building utilities for data analytics and query engines, or building full pipelines with tabular data, this book can help you out regardless of your preferred programming language. A basic understanding of data analysis concepts is needed, but not necessary. Code examples are provided using C++, Python, and Go throughout the book.Table of Contents- Getting Started with Apache Arrow- Working with Key Arrow Specifications- Format and Memory Handling- Crossing the Language Barrier with the Arrow C Data API- Acero: A Streaming Arrow Execution Engine- Using the Arrow Datasets API- Exploring Apache Arrow Flight RPC- Understanding Arrow Database Connectivity (ADBC)- Using Arrow with Machine Learning Workflows- Powered by Apache Arrow- How to Leave Your Mark on Arrow- Future Development and Plans
AI-Powered Developer
Use groundbreaking generative AI tools to increase your productivity, efficiency, and code quality. AI coding tools like ChatGPT and GitHub Copilot are changing the way we write code and build software. AI-Powered Developer reveals the practical best practices you need to deliver reliable results with AI. It cuts through the hype, showcasing real-world examples of how these tools ease and enhance your everyday tasks, and make you more creative. In AI-Powered Developer you'll discover how to get the most out of AI: - Harness AI to help you design and plan software - Use AI for code generation, debugging, and documentation - Improve your code quality assessments with the help of AI - Articulate complex problems to prompt an AI solution - Develop a continuous learning mindset that keeps you up to date - Adapt your development skills to almost any language AI coding tools give you a smart and reliable junior developer that's fast and keen to help out with your every task and query. AI-Powered Developer helps you put your new assistant to work. You'll learn to use AI for everything from writing boilerplate, to testing and quality assessment, managing infrastructure, delivering security, and even assisting with software design. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Using AI tools like Copilot and ChatGPT is like hiring a super-smart and super-fast junior developer eager to take on anything from research to refactoring. Coding with AI can help you work faster, write better applications, and maybe do things that aren't even possible with your current team. This book will show you how. About the book AI-Powered Developer: Build software with ChatGPT and Copilot teaches you in concrete detail how to maximize the impact of AI coding tools in real-world software development. In it, you'll walk through a complete application, introducing AI into every step of the workflow. You'll use ChatGPT and Copilot to generate code and ideas, make predictive suggestions, and develop a self-documenting application. You'll also learn how AI can help test and explain your code. What's inside - Use AI to design and plan software - Code generation, debugging, and documentation - Improve code quality assessments - Work with unfamiliar programming languages About the reader For intermediate software developers. No AI experience necessary. About the author Nathan B. Crocker is Cofounder and CTO at Checker Corp. The technical editor on this book was Nicolai Nielsen. Table of Contents PART 1 1 Understanding large language models 2 Getting started with large language models PART 2 3 Designing software with ChatGPT 4 Building software with GitHub Copilot 5 Managing data with GitHub Copilot and Copilot Chat PART 3 6 Testing, assessing, and explaining with large language models PART 4 7 Coding infrastructure and managing deployments 8 Secure application development with ChatGPT 9 GPT-ing on the go A Setting up ChatGPT B Setting up GitHub Copilot C Setting up AWS CodeWhisperer
Data Storytelling with Altair and AI
Great data presentations tell a story. Learn how to organize, visualize, and present data using Python, generative AI, and the cutting-edge Altair data visualization toolkit. Take the fast track to amazing data presentations! Data Storytelling with Altair and AI introduces a stack of useful tools and tried-and-tested methodologies that will rapidly increase your productivity, streamline the visualization process, and leave your audience inspired. In Data Storytelling with Altair and AI you'll discover: - Using Python Altair for data visualization - Using Generative AI tools for data storytelling - The main concepts of data storytelling - Building data stories with the DIKW pyramid approach - Transforming raw data into a data story Data Storytelling with Altair and AI teaches you how to turn raw data into effective, insightful data stories. You'll learn exactly what goes into an effective data story, then combine your Python data skills with the Altair library and AI tools to rapidly create amazing visualizations. Your bosses and decision-makers will love your new presentations--and you'll love how quick Generative AI makes the whole process! Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Every dataset tells a story. After you've cleaned, crunched, and organized the raw data, it's your job to share its story in a way that connects with your audience. Python's Altair data visualization library, combined with generative AI tools like Copilot and ChatGPT, provide an amazing toolbox for transforming numbers, code, text, and graphics into intuitive data presentations. About the book Data Storytelling with Altair and AI teaches you how to build enhanced data visualizations using these tools. The book uses hands-on examples to build powerful narratives that can inform, inspire, and motivate. It covers the Altair data visualization library, along with AI techniques like generating text with ChatGPT, creating images with DALL-E, and Python coding with Copilot. You'll learn by practicing with each interesting data story, from tourist arrivals in Portugal to population growth in the USA to fake news, salmon aquaculture, and more. What's inside - The Data-Information-Knowledge-Wisdom (DIKW) pyramid - Publish data stories using Streamlit, Tableau, and Comet - Vega and Vega-Lite visualization grammar About the reader For data analysts and data scientists experienced with Python. No previous knowledge of Altair or Generative AI required. About the author Angelica Lo Duca is a researcher at the Institute of Informatics and Telematics of the National Research Council, Italy. The technical editor on this book was Ninoslav Cerkez. Table of Contents PART 1 1 Introducing data storytelling 2 Running your first data story in Altair and GitHub Copilot 3 Reviewing the basic concepts of Altair 4 Generative AI tools for data storytelling PART 2 5 Crafting a data story using the DIKW pyramid 6 From data to information: Extracting insights 7 From information to knowledge: Building textual context 8 From information to knowledge: Building the visual context 9 From knowledge to wisdom: Adding next steps PART 3 10 Common issues while using generative AI 11 Publishing the data story A Technical requirements B Python pandas DataFrameC Other chart types
Learned Query Optimizers
This monograph presents recent progress on using machine learning techniques to improve query optimizers in database systems. Centering around a generic paradigm of learned query optimizers, the publication covers several lines of efforts on rebuilding or aiding important components in query optimizers (i.e., cardinality estimators, cost models, and plan enumerators) with machine learning. Some important machine learning tools that have recently been developed are introduced, which are useful for query optimization, and it is shown how they are adapted for sub-tasks of query optimization. This monograph is for readers who are already familiar with query optimization and who are eager to understand what machine learning techniques can be helpful, and how to apply them with examples and necessary details. The text is also relevant for machine learning researchers who want to expand their research agendas to helping database systems with machine learning techniques. Some open research challenges are also discussed with the goal of making learned query optimizers truly applicable in production.
Intelligent Modeling, Prediction, and Diagnosis from Epidemiological Data
Intelligent Modelling, Prediction, and Diagnosis from Epidemiological Data COVID-19 and Beyond is a handy treatise to elicit and elaborate possible intelligent mechanisms for modeling, prediction, diagnosis and early detection of diseases arising out of outbreaks of different epidemics with special reference to COVID-19
