Download or read book Developing Kaggle Notebooks written by Gabriel Preda and published by Packt Publishing Ltd. This book was released on 2023-12-27 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: Printed in Color Develop an array of effective strategies and blueprints to approach any new data analysis on the Kaggle platform and create Notebooks with substance, style and impact Leverage the power of Generative AI with Kaggle Models Purchase of the print or Kindle book includes a free PDF eBook Key Features Master the basics of data ingestion, cleaning, exploration, and prepare to build baseline models Work robustly with any type, modality, and size of data, be it tabular, text, image, video, or sound Improve the style and readability of your Notebooks, making them more impactful and compelling Book DescriptionDeveloping Kaggle Notebooks introduces you to data analysis, with a focus on using Kaggle Notebooks to simultaneously achieve mastery in this fi eld and rise to the top of the Kaggle Notebooks tier. The book is structured as a sevenstep data analysis journey, exploring the features available in Kaggle Notebooks alongside various data analysis techniques. For each topic, we provide one or more notebooks, developing reusable analysis components through Kaggle's Utility Scripts feature, introduced progressively, initially as part of a notebook, and later extracted for use across future notebooks to enhance code reusability on Kaggle. It aims to make the notebooks' code more structured, easy to maintain, and readable. Although the focus of this book is on data analytics, some examples will guide you in preparing a complete machine learning pipeline using Kaggle Notebooks. Starting from initial data ingestion and data quality assessment, you'll move on to preliminary data analysis, advanced data exploration, feature qualifi cation to build a model baseline, and feature engineering. You'll also delve into hyperparameter tuning to iteratively refi ne your model and prepare for submission in Kaggle competitions. Additionally, the book touches on developing notebooks that leverage the power of generative AI using Kaggle Models.What you will learn Approach a dataset or competition to perform data analysis via a notebook Learn data ingestion and address issues arising with the ingested data Structure your code using reusable components Analyze in depth both small and large datasets of various types Distinguish yourself from the crowd with the content of your analysis Enhance your notebook style with a color scheme and other visual effects Captivate your audience with data and compelling storytelling techniques Who this book is for This book is suitable for a wide audience with a keen interest in data science and machine learning, looking to use Kaggle Notebooks to improve their skills and rise in the Kaggle Notebooks ranks. This book caters to: Beginners on Kaggle from any background Seasoned contributors who want to build various skills like ingestion, preparation, exploration, and visualization Expert contributors who want to learn from the Grandmasters to rise into the upper Kaggle rankings Professionals who already use Kaggle for learning and competing

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Download or read book Data Science from Scratch written by Joel Grus and published by "O'Reilly Media, Inc.". This book was released on 2015-04-14 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Download or read book Information and Communication Technologies and Sustainable Development written by Stanislav Dovgyi and published by Springer Nature. This book was released on 2023-12-19 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book highlights the most important research areas in ICT, their impact on e-society, environment sustainable development, namely analytics, security, geoinformation systems, and mathematical modeling. The studies contain a discussion on artificial intelligence in various spheres of society, practical implementation of the IoT, geoinformation systems, and remote sensing of the earth. The book focuses on improving services providing, system architecture for SDN, forecasting social and environment sustainable development based on global information space, a new approach to radio electronics systems for the novel cloud infrastructure implementation. The results are used for novel systems and to promote new approaches for e-societies. The book offers a valuable resource for specialists of R&D organizations, the management of state administration who are involved in sustainable society development, professors, university lecturers, Ph.D. students, and bachelor and master degree students.

Download or read book Hands On Machine Learning with Scikit Learn Keras and TensorFlow written by Aurélien Géron and published by "O'Reilly Media, Inc.". This book was released on 2019-09-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

Download or read book Natural Language Processing with Transformers Revised Edition written by Lewis Tunstall and published by "O'Reilly Media, Inc.". This book was released on 2022-05-26 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments

Download or read book Developing Sustainable and Energy Efficient Software Systems written by Artem Kruglov and published by Springer Nature. This book was released on 2023-02-06 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides information how to choose and collect the appropriate metrics for a software project in an organization. There are several kinds of metrics, based on the analysis of source code and developed for different programming paradigms such as structured programming and object-oriented programming (OOP). This way, the book follows three main objectives: (i) to identify existing and easily-collectible measures, if possible in the early phases of software development, for predicting and modeling both the traditional attributes of software systems and attributes specifically related to their efficient use of resources, and to create new metrics for such purposes; (ii) to describe ways to collect these measures during the entire lifecycle of a system, using minimally-invasive monitoring of design-time processes, and consolidate them into conceptual frameworks able to support model building by using a variety of approaches, including statistics, data mining and computational intelligence; and (iii) to present models and tools to support design time evolution of systems based on design-time measures and to empirically validate them. The book provides researchers and advanced professionals with methods for understanding the full implications of alternative choices and their relative attractiveness in terms of enhancing system resilience. It also explores the simultaneous use of multiple models that reflect different system interpretations or stakeholder perspectives.

Download or read book Developing and Monitoring Smart Environments for Intelligent Cities written by Mahmood, Zaigham and published by IGI Global. This book was released on 2020-11-20 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, intelligent cities, also known as smart cities or cognitive cities, have become a perceived solution for improving the quality of life of citizens while boosting the efficiency of city services and processes. This new vision involves the integration of various sectors of society through the use of the internet of things. By continuing to enhance research for the better development of the smart environments needed to sustain intelligent cities, citizens will be empowered to provision the e-services provided by the city, city officials will have the ability to interact directly with the community as well as monitor digital environments, and smart communities will be developed where citizens can enjoy improved quality of life. Developing and Monitoring Smart Environments for Intelligent Cities compiles the latest research on the development, management, and monitoring of digital cities and intelligent environments into one complete reference source. The book contains chapters that examine current technologies and the future use of internet of things frameworks as well as device connectivity approaches, communication protocols, security challenges, and their inherent issues and limitations. Including unique coverage on topics such as connected vehicles for smart transportation, security issues for smart homes, and building smart cities for the blind, this reference is ideal for practitioners, urban developers, urban planners, academicians, researchers, and students.

Download or read book Proceedings of the 7th International Conference on Economic Management and Green Development written by Xiaolong Li and published by Springer Nature. This book was released on with total page 2095 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Machine Learning with PyTorch and Scikit Learn written by Sebastian Raschka and published by Packt Publishing Ltd. This book was released on 2022-02-25 with total page 775 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Download or read book Approaching Almost Any Machine Learning Problem written by Abhishek Thakur and published by Abhishek Thakur. This book was released on 2020-07-04 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is not a traditional book. The book has a lot of code. If you don't like the code first approach do not buy this book. Making code available on Github is not an option. This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book doesn't explain the algorithms but is more oriented towards how and what should you use to solve machine learning and deep learning problems. The book is not for you if you are looking for pure basics. The book is for you if you are looking for guidance on approaching machine learning problems. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along. Table of contents: - Setting up your working environment - Supervised vs unsupervised learning - Cross-validation - Evaluation metrics - Arranging machine learning projects - Approaching categorical variables - Feature engineering - Feature selection - Hyperparameter optimization - Approaching image classification & segmentation - Approaching text classification/regression - Approaching ensembling and stacking - Approaching reproducible code & model serving There are no sub-headings. Important terms are written in bold. I will be answering all your queries related to the book and will be making YouTube tutorials to cover what has not been discussed in the book. To ask questions/doubts, visit this link: https://bit.ly/aamlquestions And Subscribe to my youtube channel: https://bit.ly/abhitubesub

Download or read book AI for the Good written by Stefan H. Vieweg and published by Springer Nature. This book was released on 2021-04-24 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: While technology advances at a high pace in the age of machine learning, there is a lack of clear intent and framing of acceptable ethical standards. This book brings together the complex topic of "good" technology in a cross-functional way, alternating between theory and practice.The authors address the ever-expanding discussion on Artificial Intelligence (AI) and ethics by providing an orientation. Pragmatic and recent issues are especially taken into account such as the collateral effects of the COVID19 pandemic. An up-to-date overview of digitization - already a very broad field in itself - is presented along with an analysis of the approaches of AI from an ethical perspective. Furthermore, concrete approaches to consider appropriate ethical principles in AI-based solutions are offered. The book will be appealing to academics, from humanities or business or technical disciplines, as well as practitioners who are looking for an introduction to the topic and an orientation with concrete questions and assistance.

Download or read book Computer Programming And Software Development 9 Books In 1 written by Richie Miller and published by Richie Miller. This book was released on 2023 with total page 694 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you want to discover how to become a software developer using C#, Python, Angular, or JavaScript, this book is for you! 9 BOOKS IN 1 DEAL! · BOOK 1: ANGULAR FRAMEWORK ESSENTIALS - OPEN SOURCE WEB APP DEVELOPMENT USING ANGULAR & TYPESCRIPT · BOOK 2: PYTHON MACHINE LEARNING - ALGORITHM DESIGN & PRACTICAL CODE EXECUTION · BOOK 3: REACT JAVASCRIPT VULNERABILITIES - CONSTRUCTING SECURE REACTJS CODE · BOOK 4: C# CODING SYNTAX - C SHARP SOFTWARE DEVELOPMENT FUNDAMENTALS · BOOK 5: C# PROGRAMMING BASICS - WRITE, RUN, AND DEBUG CONSOLE APPLICATIONS · BOOK 6: C# CODING FUNDAMENTALS - CONTROL FLOW STATEMENTS AND EXPRESSIONS · BOOK 7: C# TYPE CLASS FUNDAMENTALS - BUILT-IN DATA TYPES, CLASSES, INTERFACES, AND INHERITANCE · BOOK 8: C# PROGRAMMING - EXPLICIT INTERFACE IMPLEMENTATION · BOOK 9: C# GENERICS - PERFORMANCE AND TYPE SAFETY BUY THIS BOOK NOW AND GET STARTED TODAY!

Download or read book Developing Medical Apps and mHealth Interventions written by Alan Davies and published by Springer Nature. This book was released on 2020-07-13 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a practically applicable guide to designing evidence-based medical apps and mHealth interventions. It features detailed guidance and case studies where applicable on the best practices and available techniques from both technological (platform technologies, toolkits, sensors) and research perspectives. This approach enables the reader to develop a deep understanding of how to collect the appropriate data and work with users to build a user friendly app for their target audience. Information on how researchers and designers can communicate their intentions with a variety of stakeholders including medical practitioners, developers and researchers to ensure the best possible decisions are made during the development process to produce an app of optimal quality that also considers usability. Developing Medical Apps and mHealth Interventions comprehensively covers the development of medical and health apps for researchers, informaticians and physicians, and is a valuable resource for the experienced professional and trainee seeking a text on how to develop user friendly medical apps.

Download or read book Intelligent Computing for Sustainable Development written by S. Satheeskumaran and published by Springer Nature. This book was released on with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Live Longer with AI written by Tina Woods and published by Packt Publishing Ltd. This book was released on 2020-09-30 with total page 603 pages. Available in PDF, EPUB and Kindle. Book excerpt: A wakeup call that shows us how to live our best and longest lives through the power of AI Key FeaturesDiscover how the latest cutting-edge developments in health and AI are helping us live longer, healthier, and better livesPersonalize your health, wealth and well-being using technology best suited to help you plan and build up your assets for a multi-stage lifeUnderstand how we can live our best lives in a post-COVID-19 world and equip ourselves for the next pandemic using technologyBook Description Live Longer with AI examines how the latest cutting-edge developments are helping us to live longer, healthier and better too. It compels us to stop thinking that health is about treating disease and start regarding it as our greatest personal and societal asset to protect. The book discusses the impact that AI has on understanding the cellular basis of aging and how our genes are influenced by our environment – with the pandemic highlighting the interconnectedness of human and planetary health. Author Tina Woods, founder and CEO of Collider Health and Collider Science, and the co-founder of Longevity International, has curated a panel of deeply insightful interviews with some of today’s brightest and most innovative thought leaders at the crossroads of health, technology and society. Read what leading experts in health and technology are saying about the book: "This is a handbook for the revolution!" —Sir Muir Gray, Director, Optimal Ageing "You can live longer and be happier if you make some changes – that is the theme of this book. Well-written and compelling." —Ben Page, CEO, Ipsos Mori "Tina's book is a must-read for those who want to discover the future of health." —José Luis Cordeiro, Fellow, World Academy of Art & Science; Director, The Millennium Project; Vice Chair, Humanity Plus; Co-Author of The Death of Death About the consultant editor Melissa Ream is a leading health and care strategist in the UK, leveraging user-driven design and artificial intelligence to design systems and support people to live healthier, longer lives. What you will learnDiscover how AI is changing the way we understand the wider determinants of health, how the environment influences our genes and why the solutions for living longer are linked to living greenerInform your perspective on how technology can deal with the health emergency in front of us – by minimizing health and wealth inequalitiesLearn why our “life data” is so important and how sharing it will help us develop aging “bio-markers”, enabling us to predict and manage dementia and other chronic diseases of agingFind out how scientists and doctors are using AI to find a vaccine for Covid-19, make us more resilient to future pandemic threats and pre-empt the next outbreakWho this book is for Professionals and general readers with an interest in learning how technology can and is being used to change our approach to aging and help us live longer and healthier lives. No prior knowledge of or experience with artificial intelligence is required.

Download or read book AI Powered Productivity written by Asma Asfour and published by Asma Asfour. This book was released on 2024-08-06 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: AI-Powered Productivity is a guide to understanding and using AI and generative tools in professional settings. Chapter 1 introduces AI basics, its impact on various sectors, and an overview of generative AI tools. Chapter 2 delves into large language models exploring their integration with multimodal technologies and effects on productivity. Chapter 3 offers a practical guide to mastering LLM prompting and customization, with tutorials on crafting effective prompts and advanced techniques, including real-world examples of AI applications. Chapter 4 examines how AI can enhance individual productivity, focusing on professional and personal benefits, ethical use, and future trends. Chapter 5 addresses data-driven decision-making, covering data analysis techniques, AI in trend identification, consumer behavior analysis, strategic planning, and product development. Chapter 6 discusses strategic and ethical considerations, including AI feasibility, tool selection, multimodal workflows, and best practices for ethical AI development and deployment. Chapter 7 highlights AI's role in transforming training and professional development, covering structured training programs, continuous learning initiatives, and fostering a culture of innovation and experimentation. Chapter 8 provides a guide to successfully implementing AI in organizations, discussing team composition, collaborative approaches, iterative development processes, and strategic alignment for AI initiatives. Finally, Chapter 9 looks ahead to the future of work, preparing readers for the AI revolution by addressing training and education, career paths, common fears, and future workforce trends. This book is designed for both beginners and professionals, offering a deep dive into AI concepts, tools, and practices that define the current AI landscape.