“…Conference sessions include: Automated Insights’ Robbie Allen on the future of natural language generation over the next 10 years; Intel’s Vin Sharma on the company’s investment in open AI solutions for the autonomous driving, healthcare, and financial services industries; UC Berkeley’s Pieter Abbeel on reinforcement learning in robotics; Preferred Networks’ Shohei Hido on a Python framework for complex neural networks; Google’s Martin Wicke on the TensorFlow-based APIs that will democratize machine learning; and Cortical.io’s Francisco Webber on semantic folding, an alternative to the big data machine learning approach to AI. O'Reilly Artificial Intelligence Conference Total access to each of the 13 keynotes and 42 sessions delivered at AI NY 2016 Energized discourse by 66 AI experts from 39 of the world’s top AI companies and research groups High-level briefings from MIT, HKUST, UCB, Stanford, and the Allen Institute for Artificial Intelligence Demos of Capital One’s CI tool for cybersecurity and Intel’s Xeon Phi machine learning product line Strategic advisories from FirstMark Capital, HyperScience, McKinsey, and The Longevity Fund Deep learning updates from TensorFlow, Enlitic, Algorithmia, and Baidu’s Silicon Valley AI Lab Demos of NVIDIA’s neural network tool DIGITS and x.ai’s AI personal assistant "Amy" Insider looks at Microsoft’s Project Malmo and the deep learning toolkit CNTK Overviews of breakthroughs in CNN based image, speech and emotion recognition…”

“…This book is a comprehensive guide to designing and architecting enterprise-grade streaming applications using Apache Kafka and other big data tools. It includes best practices for building such applications, and tackles some common challenges such as how to use Kafka efficiently and handle high data volumes with ease. …”

“…Learn how to utilize SQLite to collect and analyze data from multiple systems In Detail Modern web technology and portable computing together have enabled huge advances in the Internet of Things (IoT) space,as well as in areas such as machine learning and big data. The Raspberry Pi is a very popular portable computer for running full stack web applications. …”

“…Guenther Dobrauz, Partner with PwC in Zurich and Leader of PwC Legal Switzerland Barmak Meftah, President, ATamp;T Cybersecurity Cleve Adams, CEO, Site 1001 (AI and big data based smart building company) Ann Johnson, Corporate Vice President - Cybersecurity Solutions Group, Microsoft Barbara Humpton, CEO, Siemens USA Businesses and states depend on effective cybersecurity. …”

“…You’ll watch him demonstrate prediction, classification, decision trees, and cluster analysis...key algorithms such as nearest neighbor...artificial neural networks...regression and time-series forecasting...text analytics and sentiment analysis...big data techniques, technologies, and more. In just hours, you’ll be ready to analyze huge volumes of data, discover crucial new insights, and make better, faster decisions! …”

“…Other LiveLessons videos from Brien Posey include, Practical Windows PowerShell Scripting ; Microsoft Exchange Server 2016 ; Building, Managing, and Migrating Virtual Machines with Hyper-V and Azure , and most recently; PowerShell for Business Intelligence and Big Data Analytics . Skill Level Beginner to Intermediate Learn How To Deploy Microsoft Exchange Deploy System Center Virtual Machine Manager Create a hybrid Exchange Server deployment with Office 365 Configure Office 365 to use Azure AD Create virtual machines in Azure and Hyper-V and manage them using Virtual Machine Manager Manage mobile devices using Intune Who Should Take This Course Anyone who needs to make their on-premises Microsoft servers work with public cloud services such as Office 365, Microsoft Azure, or Microsoft Intune Course Requirements A basic understanding of Windows Server and general networking. …”

“…In the Intro to Data Science videos, Paul lays the groundwork for later lessons in which he’ll introduce some of today’s most compelling, leading-edge computing technologies, including natural language processing, data mining Twitter® for sentiment analysis, cognitive computing with IBM® WatsonTM, supervised machine learning with classification and regression, unsupervised machine learning with clustering, computer vision through deep learning and convolutional neural networks, sentiment analysis through deep learning with recurrent neural networks, big data with Hadoop®, SparkTM streaming, NoSQL databases and the Internet of Things. …”

“…Some session of note include: Bayesian Statistics Made Simple Gradient Descent, Demystified Comparing Models Using Resampling and Bayesian Methods Next Generation Indexes For Big Data Engineering Probabilistic Programming with PyMC3 Racial Bias in Facial Recognition Software Visualization throughout the Data Science Workflow: Datafy All The Things Data Science, Management..…”

“…Deep dive tutorials, including Jason Dai (Intel) on building deep learning apps for big data with the Analytics Zoo AI platform; Chaoran Yu (Lightbend) on doing machine learning (ML) with Kafka-based streaming pipelines; and Justina Petraityte (Rasa) on developing intelligent AI assistants based entirely on ML with open source Rasa NLU and Rasa Core. …”

“…As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. …”

“…All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. …”

“…All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. …”

“…You will have the knowledge and skills required to do the following: Architect, engineer, integrate, and implement secure solutions across complex environments to support a resilient enterprise This course in the CASP series covers the following topics: Architecture Analyze security requirements and objectives to ensure an appropriate, secure network architecture for a new or existing network Services Deperimeterization/zero trust Merging of networks from various organizations Software-defined networking Analyze organizational requirements to determine proper infrastructure security design Scalability Resiliency Automation Performance Containerization Virtualization Content delivery network Caching Integrate software applications securely into an enterprise architecture Baseline and templates Software assurance Considerations of integrating enterprise applications Integrating security into development life cycle Implement data security techniques for securing enterprise architecture Data loss prevention Data loss detection Data classification, labeling, and tagging Obfuscation Anonymization Encrypted versus unencrypted Data life cycle Data inventory and mapping Data integrity management Data storage, backup, and recovery Analyze security requirements and objectives to provide appropriate authentication and authorization controls Credential management Password policies Federation Access control Protocols Multifactor authentication One-time password Hardware root of trust Single sign-on JavaScript Object Notation web token Attestation and identity proofing Implement secure cloud and virtualization solutions Virtualization strategies Provisioning and deprovisioning Middleware Metadata and tags Deployment models and considerations Hosting models Service models Cloud provider limitations Extending appropriate on-premises controls Storage models Explain how cryptography and public key infrastructure support security objectives and requirements Privacy and confidentiality requirements Integrity requirements Non-repudiation Compliance and policy requirements Common cryptography use cases Common Public key infrastructure use cases Explain impact of emerging technologies on enterprise security and privacy Artificial intelligence Machine learning Quantum computing Blockchain Homomorphic encryption Secure multiparty computation Distributed consensus Big Data Virtual reality Three Dimetional printing Passwordless authentication Nano technology Deep learning Biometric impersonation…”

“…., precision/personalized medicine, biomarker-driven target clinical trials, model informed drug development, big data analytics, and real world data/evidence).This book provides key statistical concepts, innovative designs, and analysis methods that are useful in regulatory science. …”

“…Necesita aprender de SEO, SEM, Social Media, branded content, display advertising, del big data, del internet of things, y de un sinfín de nuevas herramientas. …”

“…Esta transformación, junto a la necesaria orientación al valor y a los valores, es lo que justifica el título dado a la obra: Investigación de marketing 3.0.El libro se ha escrito con el objetivo de responder a la necesidad de conocer el proceso de investigación en marketing en esta situación de mundos superpuestos, tradicional y digital, aunque cada vez más digital.El libro parte del análisis de la investigación de marketing en una sociedad y economía de la información, de la importancia de la investigación para adquirir conocimiento, pero también como sector económico, de los aspectos éticos y códigos de conducta en la investigación y de las tendencias de futuro (nuevos datos, nuevos procedimientos, nuevas habilidades, big data, etc.). En el capítulo dos se tratan los sistemas de información, con especial referencia a la función de marketing, de su evolución y tendencias. …”

“…Asimismo, es Investigadora Asociada del Overseas Development Institute (ODI), en Londres, generando informes basados en big data e inteligencia artificial; de Datactive, de la Universidad de Amsterdam, y de MediaData, de la Universidad Politécnica de Valencia. …”

“…You'll also see how cloud platforms and third-party integrations enable endless possibilities for your end-users, such as insights with big data analytics and predictive maintenance to minimize costs.By the end of this book, you'll have developed the skills you need to start using ESP32 in your next wireless IoT project and meet the project's requirements by building effective, efficient, and secure solutions.What You Will Learn:Explore advanced use cases like UART communication, sound and camera features, low-energy scenarios, and scheduling with an RTOSAdd different types of displays in your projects where immediate output to users is requiredConnect to Wi-Fi and Bluetooth for local network communicationConnect cloud platforms through different IoT messaging protocolsIntegrate ESP32 with third-party services such as voice assistants and IFTTTDiscover best practices for implementing IoT security features in a production-grade solutionWho this book is for:If you are an embedded software developer, an IoT software architect or developer, a technologist, or anyone who wants to learn how to use ESP32 and its applications, this book is for you. …”
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“…Finally, you'll gain a clear understanding of Redshift use cases, data ingestion, data management, security, and scaling so that you can build a scalable data warehouse platform.By the end of this Redshift book, you'll be able to implement a Redshift-based data analytics solution and have understood the best practice solutions to commonly faced problems.What You Will Learn:Use Amazon Redshift to build petabyte-scale data warehouses that are agile at scaleIntegrate your data warehousing solution with a data lake using purpose-built features and services on AWSBuild end-to-end analytical solutions from data sourcing to consumption with the help of useful recipesLeverage Redshift's comprehensive security capabilities to meet the most demanding business requirementsFocus on architectural insights and rationale when using analytical recipesDiscover best practices for working with big data to operate a fully managed solutionWho this book is for:This book is for anyone involved in architecting, implementing, and optimizing an Amazon Redshift data warehouse, such as data warehouse developers, data analysts, database administrators, data engineers, and data scientists. …”

“…This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins.By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments.What You Will Learn:Develop your machine learning project locally with MLflow's different featuresSet up a centralized MLflow tracking server to manage multiple MLflow experimentsCreate a model life cycle with MLflow by creating custom modelsUse feature streams to log model results with MLflowDevelop the complete training pipeline infrastructure using MLflow featuresSet up an inference-based API pipeline and batch pipeline in MLflowScale large volumes of data by integrating MLflow with high-performance big data librariesWho this book is for:This book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. …”