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This is the workbook for Garrett Wasny's Advanced Googling professional development seminar. He delivers the course online and in-person to accountants, lawyers, doctors, engineers, pro sports executives and other elite knowledge workers worldwide. In easy-to-understand and non-techical language, the course and manual explain how to: Customize Google for maximum speed, security and style Utilize productivity-enhancing apps and plug-ins that instantly enhance your Google experience and performance Scan Google with added precision, nuance, speed and confidence Discover literally 10x more information that’s hiding in plain sight on the Google search results page Compose advanced search queries that generate more relevant results Automatically and continuously monitor your operational landscape using free alert and aggregation services Use Google’s new generation of predictive apps that know what you want without you having to ask Use little-known hot-words and commands to uncover concealed Google signals Creatively use language in Google search strings to boost relevancy Transform Google into your backup brain, robot assistant and ambient sidekick Leverage Google hundreds of ways to improve your online research, collaboration and communications in your professional and personal life
This free 15-hour course explained the ocean depths, the properties of the water, ocean circulation and how the oceans influence climate.
Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including: • Monte Carlo Stock Price Simulation • Image Denoising using Mean-Field Variational Inference • EM algorithm for Hidden Markov Models • Imbalanced Learning, Active Learning and Ensemble Learning • Bayesian Optimization for Hyperparameter Tuning • Dirichlet Process K-Means for Clustering Applications • Stock Clusters based on Inverse Covariance Estimation • Energy Minimization using Simulated Annealing • Image Search based on ResNet Convolutional Neural Network • Anomaly Detection in Time-Series using Variational Autoencoders Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. You’ll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they’re put into action. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods. About the book Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You’ll especially appreciate author Vadim Smolyakov’s clear interpretations of Bayesian algorithms for Monte Carlo and Markov models. What's inside • Monte Carlo stock price simulation • EM algorithm for hidden Markov models • Imbalanced learning, active learning, and ensemble learning • Bayesian optimization for hyperparameter tuning • Anomaly detection in time-series About the reader For machine learning practitioners familiar with linear algebra, probability, and basic calculus. About the author Vadim Smolyakov is a data scientist in the Enterprise & Security DI R&D team at Microsoft. Table of Contents PART 1 1 Machine learning algorithms 2 Markov chain Monte Carlo 3 Variational inference 4 Software implementation PART 2 5 Classification algorithms 6 Regression algorithms 7 Selected supervised learning algorithms PART 3 8 Fundamental unsupervised learning algorithms 9 Selected unsupervised learning algorithms PART 4 10 Fundamental deep learning algorithms 11 Advanced deep learning algorithms
The updated new edition of the classic strategic analysis textbook, addresses the emerging challenges that businesses face in an increasingly complex and dynamic environment A market-leading title across the world in upper-level undergraduate and MBA courses, Contemporary Strategy Analysis combines a rigorous approach to business strategy analysis with highly relevant examples of current practice and a rich collection of case studies. With a lively narrative style, expert strategist and lecturer Robert M. Grant introduces students to some of the latest thinking in the strategy field while addressing the strategic issues that business enterprises face today. Designed to be accessible for students from different backgrounds with varying levels of experience, the twelfth edition of Contemporary Strategy Analysis builds upon its tradition of clarity of exposition, emphasis on practicality, and concentration on the fundamentals of value creation and appropriation. A wide range of contemporary topics are presented alongside fully integrated business models, real-world examples of business model innovation, and practical tools for formulating and implementing business strategies. Contemporary Strategy Analysis, Twelfth Edition, remains the ideal textbook for core strategy courses in Business Studies, Management, and MBA programs, as well as for executive courses on competitive strategy, corporate strategy, and management consulting skills. It is also a valuable resource for managers, business owners, founders, and other professionals who are responsible for the strategic direction of a modern enterprise.
With this book, you'll learn how to take full advantage of Google AdWords and AdSense, the sophisticated online advertising tools used by thousands of large and small businesses. This new edition provides a substantially updated guide to advertising on the Web, including how it works in general, and how Google's advertising programs in particular help you make money. You'll find everything you need to work with AdWords, which lets you generate text ads to accompany specific search term results, and AdSense, which automatically delivers precisely targeted text and image ads to your website. Google Advertising Tools focuses on best practices, with several case studies that demonstrate which approaches work well, which don't, and why. Google's ad programs can help any business with a web presence, and this guide explains precisely how to use them. Learn how to create effective campaign plans for your website Understand the PageRank algorithm, Search Engine Optimization (SEO), and Search Engine Marketing (SEM) Drive traffic to your website and make money as an advertising host Add AdSense code and Google search to your site Learn how content, search, and referral ads perform Create and edit AdWord campaigns Monitor AdWords activity and improve your campaign's performance
Inside the Dark Web provides a broad overview of emerging digital threats and computer crimes, with an emphasis on cyberstalking, hacktivism, fraud and identity theft, and attacks on critical infrastructure. The book also analyzes the online underground economy and digital currencies and cybercrime on the dark web. The book further explores how dark web crimes are conducted on the surface web in new mediums, such as the Internet of Things (IoT) and peer-to-peer file sharing systems as well as dark web forensics and mitigating techniques. This book starts with the fundamentals of the dark web along with explaining its threat landscape. The book then introduces the Tor browser, which is used to access the dark web ecosystem. The book continues to take a deep dive into cybersecurity criminal activities in the dark net and analyzes the malpractices used to secure your system. Furthermore, the book digs deeper into the forensics of dark web, web content analysis, threat intelligence, IoT, crypto market, and cryptocurrencies. This book is a comprehensive guide for those who want to understand the dark web quickly. After reading Inside the Dark Web, you’ll understand The core concepts of the dark web. The different theoretical and cross-disciplinary approaches of the dark web and its evolution in the context of emerging crime threats. The forms of cybercriminal activity through the dark web and the technological and "social engineering" methods used to undertake such crimes. The behavior and role of offenders and victims in the dark web and analyze and assess the impact of cybercrime and the effectiveness of their mitigating techniques on the various domains. How to mitigate cyberattacks happening through the dark web. The dark web ecosystem with cutting edge areas like IoT, forensics, and threat intelligence and so on. The dark web-related research and applications and up-to-date on the latest technologies and research findings in this area. For all present and aspiring cybersecurity professionals who want to upgrade their skills by understanding the concepts of the dark web, Inside the Dark Web is their one-stop guide to understanding the dark web and building a cybersecurity plan.
Free speech, privacy and truth on the internet are linked in a messy, unruly way that needs to be embraced.
A concise and practical exploration of key topics and applications in data science In Deep Learning, from Big Data to Artificial Intelligence, expert researcher Dr. Stéphane Tufféry delivers an insightful discussion of the applications of deep learning and big data that focuses on practical instructions on various software tools and deep learning methods relying on three major libraries: MXNet, PyTorch, and Keras-TensorFlow. In the book, numerous, up-to-date examples are combined with key topics relevant to modern data scientists, including processing optimization, neural network applications, natural language processing, and image recognition. This is a thoroughly revised and updated edition of a book originally released in French, with new examples and methods included throughout. Classroom-tested and intuitively organized, Deep Learning, from Big Data to Artificial Intelligence offers complimentary access to a companion website that provides R and Python source code for the examples offered in the book. Readers will also find: A thorough introduction to practical deep learning techniques with explanations and examples for various programming libraries Comprehensive explorations of a variety of applications for deep learning, including image recognition and natural language processing Discussions of the theory of deep learning, neural networks, and artificial intelligence linked to concrete techniques and strategies commonly used to solve real-world problems Perfect for graduate students studying data science, big data, deep learning, and artificial intelligence, Deep Learning, from Big Data to Artificial Intelligence will also earn a place in the libraries of data science researchers and practicing data scientists.
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Hydropedology is a microcosm for what is happening in Soil Science. Once a staid discipline found in schools of agriculture devoted to increasing crop yield, soil science is transforming itself into an interdisciplinary mulch with great significance not only for food production but also climate change, ecology, preservation of natural resources, forestry, and carbon sequestration. Hydropedology brings together pedology (soil characteristics) with hydrology (movement of water) to understand and achieve the goals now associated with modern soil science. - The first book of its kind in the market - Highly interdisciplinary, involving new thinking and synergistic approaches - Stimulating case studies demonstrate the need for hydropedology in various practical applications - Future directions and new approaches are present to advance this emerging interdisciplinary science