Information architecture

Take your Python text processing skills to another level by learning about the latest natural language processing and machine learning techniques with this full color guideKey FeaturesLearn how to acquire and process textual data and visualize the key findingsObtain deeper insight into the most commonly used algorithms and techniques and understand their tradeoffsImplement models for solving real-world problems and evaluate their performanceBook DescriptionWith the ever-increasing demand for machine learning and programming professionals, it's prime time to invest in the field.

Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive ToolkitKey FeaturesUnderstand the fundamentals of Microsoft Cognitive Toolkit and set up the development environment Train different types of neural networks using Cognitive Toolkit and deploy it to productionEvaluate the performance of your models and improve your deep learning skillsBook DescriptionCognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft.

The Final Volume of the Groundbreaking Trilogy on Agent-Based ModelingIn this pioneering synthesis, Joshua Epstein introduces a new theoretical entity: Agent_Zero.

Learn to develop blockchain-based distributed ledgers and deploy a Hyperledger Fabric network with concrete exercises and examplesKey FeaturesUpdated with the latest features and additions that come with Hyperledger Fabric 2Write your own smart contracts and services using Java and JavaScript on a Hyperledger Fabric networkDive into real-world blockchain challenges such as integration and scalabilityBook DescriptionBlockchain with Hyperledger Fabric - Second Edition is a refreshed and extended version of the successful book on practical Hyperledger Fabric blockchain development.

Leverage the power of Python and statistical modeling techniques for building accurate predictive modelsKey FeaturesGet introduced to Python's rich suite of libraries for statistical modelingImplement regression, clustering and train neural networks from scratchIncludes real-world examples on training end-to-end machine learning systems in PythonBook DescriptionPython's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today.

Build a strong foundation of machine learning algorithms in 7 daysKey FeaturesUse Python and its wide array of machine learning libraries to build predictive models Learn the basics of the 7 most widely used machine learning algorithms within a weekKnow when and where to apply data science algorithms using this guideBook DescriptionMachine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data.

Learn to work with XRP and build applications on Ripple's blockchainKey FeaturesLearn to use Ripple's decentralized system for transfering digital assets globallyA simpilfied and shortened learning curve to understand the Ripple innovation and BlockchainTakes a hands-on approach to work with XRP - Ripple's native currencyBook DescriptionThis book starts by giving you an understanding of the basics of blockchain and the Ripple protocol.

Implement supervised, unsupervised, and generative deep learning (DL) models using Keras and Dopamine with TensorFlowKey FeaturesUnderstand the fundamental machine learning concepts useful in deep learningLearn the underlying mathematical concepts as you implement deep learning models from scratchExplore easy-to-understand examples and use cases that will help you build a solid foundation in DLBook DescriptionWith information on the web exponentially increasing, it has become more difficult than ever to navigate through everything to find reliable content that will help you get started with deep learning.

A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming languageKey FeaturesCreate, deploy, productionalize, and scale automated machine learning solutions on Microsoft AzureImprove the accuracy of your ML models through automatic data featurization and model trainingIncrease productivity in your organization by using artificial intelligence to solve common problemsBook DescriptionAutomated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time.

Progressively explore UI development with Shiny via practical examplesKey FeaturesWrite a Shiny interface in pure HTMLExplore powerful layout functions to make attractive dashboards and other intuitive interfacesGet to grips with Bootstrap and leverage it in your Shiny applicationsBook DescriptionAlthough vanilla Shiny applications look attractive with some layout flexibility, you may still want to have more control over how the interface is laid out to produce a dashboard.

Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applicationsKey FeaturesBuild independent machine learning (ML) systems leveraging the best features of R 3.

Get unique insights from your data by combining the power of SQL Server, R and PythonKey FeaturesUse the features of SQL Server 2017 to implement the data science project life cycleLeverage the power of R and Python to design and develop efficient data modelsfind unique insights from your data with powerful techniques for data preprocessing and analysisBook DescriptionSQL Server only started to fully support data science with its two most recent editions.

Foundations of Quantum Programming discusses how new programming methodologies and technologies developed for current computers can be extended to exploit the unique power of quantum computers, which promise dramatic advantages in processing speed over currently available computer systems.

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization.

Implement real-world machine learning in a microservices architecture as well as design, build, and deploy intelligent microservices systems using examples and case studiesPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesDesign, build, and run microservices systems that utilize the full potential of machine learningDiscover the latest models and techniques for combining microservices and machine learning to create scalable systemsImplement machine learning in microservices architecture using open source applications with pros and consBook DescriptionWith the rising need for agile development and very short time-to-market system deployments, incorporating machine learning algorithms into decoupled fine-grained microservices systems provides the perfect technology mix for modern systems.

Take your first steps to becoming a fully qualified data analyst by learning how to explore complex datasetsKey FeaturesMaster each concept through practical exercises and activitiesDiscover various statistical techniques to analyze your dataImplement everything you've learned on a real-world case study to uncover valuable insightsBook DescriptionEvery day, businesses operate around the clock, and a huge amount of data is generated at a rapid pace.

A practical guide for solving complex data processing challenges by applying the best optimizations techniques in Apache Spark.

Develop self-learning algorithms and agents using TensorFlow and other Python tools, frameworks, and librariesKey FeaturesLearn, develop, and deploy advanced reinforcement learning algorithms to solve a variety of tasksUnderstand and develop model-free and model-based algorithms for building self-learning agentsWork with advanced Reinforcement Learning concepts and algorithms such as imitation learning and evolution strategiesBook DescriptionReinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior based on changing requirements.

Applied Analysis of Composite Media: Analytical and Computational Approaches presents formulas and techniques that can used to study 2D and 3D problems in composites and random porous media.

Re-Inventing the Book: Challenges from the Past for the Publishing Industry chronicles the significant changes that have taken place in the publishing industry in the past few decades and how they have altered the publishing value chain and the structure of the industry itself.

The Vector-Valued Maximin

Differential Equations with Applications to Mathematical Physics

Analysis and Control of Nonlinear Infinite Dimensional Systems

Scientific Computing with Automatic Result Verification

Stability and Time-Optimal Control of Hereditary Systems

Differential Equations and Mathematical Physics: Proceedings of the International Conference held at the University of Alabama at Birmingham, March 15-21, 1990

Nonlinear Equations in the Applied Sciences

Decentralized Control of Complex Systems

Overall, our object has been to provide an applications-oriented text that is reasonably self-contained.

Constructible Sets with Applications

Constructive Order Types

The Collected Papers of Gerhard Gentzen

Intuitionistic Logic Model Theory and Forcing

SET THEORY

The Problem of Inductive Logic

Contributions to Mathematical Logic

Problems in the Philosophy of Science

Problems in the Philosophy of Mathematics

Sets, Models and Recursion Theory

A Deductive Theory of Space and Time

Aspects of Inductive Logic

Studies in Logic and the Foundations of Mathematics

Formal Systems and Recursive Functions

The Foundations of Intuitionistic Mathematics

Introduction to the Theory of Finite Automata

Languages with Expressions of Infinite Length

Computer Programming and Formal Systems

The book consists of a selection of the forms of the axiom of choice which appeared in the literature together with additional forms which were obtained in the process of writing the book.

Provability, Computability and Reflection

Provability, Computability and Reflection