The Google Model

A strong business model is the bedrock to business success. But all too often, we fail to adapt, clinging to outdated business models that are no longer delivering the results we need. The brains behind The Business Model Navigator have discovered that just 55 business models are responsible for 90% of our most successful businesses. These 55 models – from the Add-On model used by Ryanair to the Subscription model used by Spotify – provide the blueprints you need to revolutionise your business, spark innovation and drive powerful change. As well as providing a practical framework for adapting and innovating your business model, this book also includes each of the 55 models in a quick-read format that covers: What it is Who invented it and who uses it now When and how to apply it The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed.

Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your results Know the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers

Kickstart your NLP journey by exploring BERT and its variants such as ALBERT, RoBERTa, DistilBERT, VideoBERT, and more with Hugging Face's transformers library Key FeaturesExplore the encoder and decoder of the transformer modelBecome well-versed with BERT along with ALBERT, RoBERTa, and DistilBERTDiscover how to pre-train and fine-tune BERT models for several NLP tasksBook Description BERT (bidirectional encoder representations from transformer) has revolutionized the world of natural language processing (NLP) with promising results. This book is an introductory guide that will help you get to grips with Google's BERT architecture. With a detailed explanation of the transformer architecture, this book will help you understand how the transformer’s encoder and decoder work. You’ll explore the BERT architecture by learning how the BERT model is pre-trained and how to use pre-trained BERT for downstream tasks by fine-tuning it for NLP tasks such as sentiment analysis and text summarization with the Hugging Face transformers library. As you advance, you’ll learn about different variants of BERT such as ALBERT, RoBERTa, and ELECTRA, and look at SpanBERT, which is used for NLP tasks like question answering. You'll also cover simpler and faster BERT variants based on knowledge distillation such as DistilBERT and TinyBERT. The book takes you through MBERT, XLM, and XLM-R in detail and then introduces you to sentence-BERT, which is used for obtaining sentence representation. Finally, you'll discover domain-specific BERT models such as BioBERT and ClinicalBERT, and discover an interesting variant called VideoBERT. By the end of this BERT book, you’ll be well-versed with using BERT and its variants for performing practical NLP tasks. What you will learnUnderstand the transformer model from the ground upFind out how BERT works and pre-train it using masked language model (MLM) and next sentence prediction (NSP) tasksGet hands-on with BERT by learning to generate contextual word and sentence embeddingsFine-tune BERT for downstream tasksGet to grips with ALBERT, RoBERTa, ELECTRA, and SpanBERT modelsGet the hang of the BERT models based on knowledge distillationUnderstand cross-lingual models such as XLM and XLM-RExplore Sentence-BERT, VideoBERT, and BARTWho this book is for This book is for NLP professionals and data scientists looking to simplify NLP tasks to enable efficient language understanding using BERT. A basic understanding of NLP concepts and deep learning is required to get the best out of this book.

This book is a must-have to any Design Sprint Master out there in the field running Design Sprints. It is full of best-practices and straight to the point information about the Google Ventures' Design Sprint methodology.

"Google and the Culture of Search examines the role of search technologies in shaping the contemporary digital and informational landscape. Ken Hillis and Michael Petit shed light on a culture of search in which our increasing reliance on search engines like Google, Yahoo! and Bing influences the way we navigate Web content--and how we think about ourselves and the world around us, online and off. Even as it becomes the number one internet activity, the very ubiquity of search technology naturalizes it as utilitarian and transparent--an assumption that Hillis and Petit explode in this innovative study. Commercial search engines supply an infrastructure that impacts the way we locate, prioritize, classify, and archive information on the Web, and as these search functionalities continue to make their way into our lives through mobile, GPS-based platforms and personalized results, distinctions between the virtual and the real collapse. Google--a multibillion-dollar global corporation--holds the balance of power among search providers, and the biases and individuating tendencies of its search algorithm undeniably shape our collective experience of the internet and our assumptions about the location and value of information. Google and the Culture of Search explores what is at stake for an increasingly networked culture in which search technology is a site of knowledge and power. This comprehensive study of search technology's broader implications for knowledge production and social relations is an indispensable resource for students and scholars of Internet and new media studies, the digital humanities, and information technology. "-- Provided by publisher.

Score Plus CBSE Question Bank and Sample Question Paper with Model Test Papers in Physics (Subject Code 042) CBSE Term II Exam 2021-22 for Class XII As per the latest CBSE Reduced Syllabus, Design of the Questions Paper, and the latest CBSE Sample Question Paper for the Board Examinations to be held in 2021. The latest CBSE Sample Question Paper 2020-21 (Solved) along with the marking scheme, released by the CBSE in October 2020 for the Board Examinations to be held in 2021. 10 Sample Papers (Solved) based on the latest Reduced Syllabus, Design of the Question Paper and the latest CBSE Sample Question Paper for the Board Examinations to be held in 2021. 10 Model Test Papers (Unsolved) based on the latest Reduced Syllabus, Design of the Question Paper and the latest CBSE Sample Question Paper for the Board Examinations to be held in 2021. Goyal Brothers Prakashan

Modern software development faces the problem of fragmentation of information across heterogeneous artefacts in different modelling and programming languages. In this dissertation, the Vitruvius approach for view-based engineering is presented. Flexible views offer a compact definition of user-specific views on software systems, and can be defined the novel ModelJoin language. The process is supported by a change metamodel for metamodel evolution and change impact analysis.

With today’s dynamic and rapidly evolving environment, media managers must have a clear understanding of different delivery platforms, as well as a grasp of critical management, planning, and economic factors in order to stay current and move their organizations forward. Developed for students in telecommunications management, media management, and the business of media, this text helps future media professionals understand the relationship and convergence patterns between the broadcast, cable television, telephony, and Internet communication industries. The second edition includes updated research throughout , including material on major business and technology changes and the importance of digital lifestyle reflected in e-commerce and personalized media selection, such as Netflix and iTunes, and the growing importance of Facebook and social networking from a business perspective.