Deep Search - Politik des Suchens jenseits von Google:Politik des Suchens jenseits von Google
A FINANCIAL TIMES BOOK OF THE MONTH FROM THE WALL STREET JOURNAL: ´´ Nothing Mr. Gilder says or writes is ever delivered at anything less than the fullest philosophical decibel.. . Mr. Gilder sounds less like a tech guru than a poet, and his words tumble out in a romantic cascade.´´ ´´Google´s algorithms assume the world´s future is nothing more than the next moment in a random process. George Gilder shows how deep this assumption goes, what motivates people to make it, and why it´s wrong: the future depends on human action.´´ - Peter Thiel, founder of PayPal and Palantir Technologies and author of Zero to One: Notes on Startups, or How to Build the Future The Age of Google, built on big data and machine intelligence, has been an awesome era. But it´s coming to an end. In Life after Google, George Gilder-the peerless visionary of technology and culture-explains why Silicon Valley is suffering a nervous breakdown and what to expect as the post-Google age dawns. Google´s astonishing ability to ´´search and sort´´ attracts the entire world to its search engine and countless other goodies-videos, maps, email, calendars....And everything it offers is free, or so it seems. Instead of paying directly, users submit to advertising. The system of ´´aggregate and advertise´´ works-for a while-if you control an empire of data centers, but a market without prices strangles entrepreneurship and turns the Internet into a wasteland of ads. The crisis is not just economic. Even as advances in artificial intelligence induce delusions of omnipotence and transcendence, Silicon Valley has pretty much given up on security. The Internet firewalls supposedly protecting all those passwords and personal information have proved hopelessly permeable. The crisis cannot be solved within the current computer and network architecture. The future lies with the ´´cryptocosm´´-the new architecture of the blockchain and its derivatives. Enabling cryptocurrencies such as bitcoin and ether, NEO and Hashgraph, it will provide the Internet a secure global payments system, ending the aggregate-and-advertise Age of Google. Silicon Valley, long dominated by a few giants, faces a ´´great unbundling,´´ which will disperse computer power and commerce and transform the economy and the Internet. Life after Google is almost here. For fans of ´´Wealth and Poverty,´´ ´´Knowledge and Power,´´ and ´´The Scandal of Money.´´
Learn how to solve challenging machine learning problems with TensorFlow, Google´s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals.
Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. KEY FEATURES ? Practical code examples ? In-depth introduction to Keras ? Teaches the difference between Deep Learning and AI ABOUT THE TECHNOLOGY Deep learning is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. AUTHOR BIO Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural networks since 2012. Francois is currently doing deep learning research at Google. He blogs about deep learning at blog.keras.io.
Deep Learning ist ein Teilbereich des Machine Learning und basiert auf künstlichen neuronalen Netzen. Dieser praktische Leitfaden bietet einen schnellen Einstieg in die Schlüsseltechnologie und erschließt Grundlagen und Arbeitsweisen von Deep Learning. Anhand Python-basierter Beispielanwendungen wird der Umgang mit den Frameworks Caffe/Caffe2 und TensorFlow gezeigt. Einfache, alltagstaugliche Beispiele laden zum Nachprogrammieren ein. Darüber hinaus erfahren Sie, warum moderne Grafikkarten, Big Data und Cloud Computing beim Deep Learning so wichtig sind. Wenn Sie bereits mit Python, NumPy und matplotlib arbeiten, ermöglicht Ihnen dieses Buch, praktische Erfahrungen mit Deep-Learning-Anwendungen zu machen. Deep Learning - die Hintergründe - Lernmethoden, die Deep Learning zugrunde liegen - Aktuelle Anwendungsfelder wie maschinelle Übersetzungen, Sprach- und Bilderkennung bei Google, Facebook, IBM oder Amazon Der Werkzeugkasten mit Docker - Der Docker-Container zum Buch: Allenötigen Tools und Programme sind bereits installiert, damit Sie die Beispiele des Buchs und eigene Deep-Learning-Anwendungen leicht ausführen können. - Die Arbeitsumgebung kennenlernen: Jupyter Notebook, Beispieldatensätze, Web Scraping Der Praxiseinstieg - Einführung in Caffe/Caffe2 und TensorFlow - Deep-Learning-Anwendungen nachprogrammieren: Handschrifterkennung, Bilderkennung und -klassifizierung, Deep Dreaming - Lösungen für Big-Data-Szenarien: verteilte Anwendungen, Spark, Cloud-Systeme - Modelle in produktive Systeme überführen Der Beispielcode und der Docker-Container zu diesem Buch stehen als Download bereit unter: https://github.com/rawar/deeplearning https://hub.docker.com/r/rawar/deeplearning/
It´s nearly impossible to build a competent Go-playing machine using conventional programming techniques, let alone have it win. By applying advanced AI techniques, in particular deep learning and reinforcement learning, users can train their Go-bot in the rules and tactics of the game. Deep Learning and the Game of Go opens up the world of deep learning and AI by teaching readers to build their own Go-playing machine. Key Features · Getting started with neural networks · Building your Go AI · Improving how your Go-bot plays and reacts Audience No deep learning experience required. All you need is high school level math and basic Python skills. This book even teaches you how to play Go! Author Bio Max Pumperla is a Data Scientist and Engineer specializing in Deep Learning at the artificial intelligence company skymind.ai. He is the cofounder of the Deep Learning platform aetros.com. Kevin Ferguson has 18 years of experience in distributed systems and data science. He is a data scientist at Honor, and has experience at companies such as Google and Meebo. Together, Max and Kevin are co-authors of betago, one of very few open source Go bots, developed in Python.
Learn fundamental to advanced GCP architectural techniques using 30 + real-world use cases. The ´Google Cloud Platform an Architect´s Guide´ is a comprehensive handbook that covers everything that you need to know from GCP fundamentals to advanced cloud architecture topics. The book covers what you need to understand to pass the Google certification exams but goes far further and deeper as it explores real-world use cases and business scenarios. But you don´t need to be an IT expert as the book is designed to cater for both beginners and those experienced in other cloud or on other on-premises networks. To that end, the book is split into distinct parts that caters for all levels of expertise. Part -1 is aimed at the novice someone new to a cloud architecture environment that needs to become familiar with the fundamentals of cloud architecture and industry best practices so the more experienced reader may wish to skip this section. Part-2 takes a far deeper dive into GCP theory and practice as well as providing real-world use cases and practical tips that are beneficial for architects at all levels. Part-3 delves much deeper into GCP practical theory on elasticity, scalability and resilience. It also covers Kubernetes in greater detail and touches on High-Performance Computing and IoT designs. The book closes with a final part dealing with cloud-native design practices and as such it covers design, monitoring, notification and remediation techniques to ensure best practice in cloud-native application design, deployment, stabilisation and commissioning.
Want Market Share? Google It! You know you´ve hit it big when your name becomes a verb - and no one knows that better than Google. In just over 10 years, Google has become the world´s most valuable brand, consistently dominating its category and generating $6 billion in revenue per quarter. How does Google do it? In a word: marketing. You may not think Google does much marketing. Indeed, it doesn´t do a lot of what has traditionally been viewed as marketing. But in today´s digital world, marketing has taken new shape - and Google is at the cutting edge. In Everything I Know about Marketing I Learned from Google, digital marketing expert Aaron Goldman offers 20 powerful lessons straight from Google´s playbook. Taking you deep into the inner workings of the Googleplex (which are simpler than you think), Goldman provides the knowledge and tools you need to build and grow your brand (which is also simpler than you think). Along the way, he shows how Google´s tactics are being used by a wide range of successful corporations, from Apple to Zappos. Key principles include: Tap into the Wisdom of Crowds: Get the signals you need directly from your customers Keep It Simple, Stupid: Craft messages people can grasp in a nanosecond and pass along Don´t Interrupt: Join the conversation - but avoid disrupting it Act Like Content: Provide value, not sales pitches Test Everything: Take no detail of your program for granted; you can always improve Show Off Your Assets: Distribute your brand everywhere The beauty of it all is that these Googley lessons can be applied to every aspect of marketing, in organizations of any size. Whether you run a PR department in a multinational corporation or serve as the sole marketer in a small business, these tactics work. In its mission to ´´organize the world´s information 1. Language: English. Narrator: Wayne Shepherd. Audio sample: http://samples.audible.de/bk/graw/000104/bk_graw_000104_sample.mp3. Digital audiobook in aax.
This is the bundle of two successful audiobooks in the market!In Deep Learning for Beginners: A Comprehensive Introduction of Deep Learning Fundamentals for Beginners to Understanding Frameworks, Neural Networks, Large Datasets, and Creative Applications with Ease, you will learn:Deep learning utilizes frameworks which allow people to develop tools able to offer better abstraction, along with simplification of hard programming issues TensorFlow and Caffe2 as the prime frameworks that are used for development by Google and Facebook Several components and tools of deep learning, such as the neural networks, CNNs, RNNs, GANs, and auto-encodersSeveral applications, including chatbots and virtual assistants, which have become the main focus for deep learning into the future, as they represent the next frontier in information gathering and connectivityAnd much moreIn Data Science from Scratch: The #1 Data Science Guide for Everything a Data Scientist Needs to Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, and Decision Trees, you will learn:In-depth information about what data science is and why it is importantThe prerequisites you will need to get started in data scienceWhat it means to be a data scientistThe roles that hacking and coding play in data scienceThe different coding languages that can be used in data science Why python is so importantHow to use linear algebra and statistics The different applications for data science. How to work with the data through munging and cleaningAnd much moreGet your copy of these fantastic audiobooks to master state-of-the-art deep-learning algorithms and their implementation! 1. Language: English. Narrator: Christopher Nieten. Audio sample: http://samples.audible.de/bk/acx0/128109/bk_acx0_128109_sample.mp3. Digital audiobook in aax.