Information architecture

Topics in Optimization

Extract accurate information from data to train and improve machine learning models using NumPy, SciPy, pandas, and scikit-learn librariesKey FeaturesDiscover solutions for feature generation, feature extraction, and feature selectionUncover the end-to-end feature engineering process across continuous, discrete, and unstructured datasetsImplement modern feature extraction techniques using Python's pandas, scikit-learn, SciPy and NumPy librariesBook DescriptionFeature engineering is invaluable for developing and enriching your machine learning models.

Build attractive, insightful, and powerful visualizations to gain quality insights from your dataKey FeaturesMaster Matplotlib for data visualizationCustomize basic plots to make and deploy figures in cloud environmentsExplore recipes to design various data visualizations from simple bar charts to advanced 3D plotsBook DescriptionMatplotlib provides a large library of customizable plots, along with a comprehensive set of backends.

Leverage Elastic Stack's machine learning features to gain valuable insight from your dataKey FeaturesCombine machine learning with the analytic capabilities of Elastic StackAnalyze large volumes of search data and gain actionable insight from themUse external analytical tools with your Elastic Stack to improve its performanceBook DescriptionMachine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting.

Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AIKey FeaturesLearn to create an operationally effective advanced analytics team in a corporate environmentSelect and undertake projects that have a high probability of success and deliver the improved top and bottom-line resultsUnderstand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your teamBook DescriptionIn Building Analytics Teams, John K.

Dynamic Programming and Stochastic Control

Leverage the power of machine learning on mobiles and build intelligent mobile applications with easeKey FeaturesBuild smart mobile applications for Android and iOS devicesUse popular machine learning toolkits such as Core ML and TensorFlow LiteExplore cloud services for machine learning that can be used in mobile appsBook DescriptionMachine learning presents an entirely unique opportunity in software development.

Presents the state of the technology and points to future directions for semantic computing Semantic computing, a rapidly evolving interdisciplinary field, seeks to structure, design, and manipulate computer content to better satisfy the needs and intentions of users and create a more meaningful user experience.

This is a softcover version of the title released in 2011; there is no new material.

Foundations of the Theory of Learning Systems

Combinatory Logic

Tackle the complex challenges faced while building end-to-end deep learning models using modern R librariesKey FeaturesUnderstand the intricacies of R deep learning packages to perform a range of deep learning tasksImplement deep learning techniques and algorithms for real-world use casesExplore various state-of-the-art techniques for fine-tuning neural network modelsBook DescriptionDeep learning (DL) has evolved in recent years with developments such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning.

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.

- Treats joint source and channel decoding in an integrated way- Gives a clear description of the problems in the field together with the mathematical tools for their solution- Contains many detailed examples useful for practical applications of the theory to video broadcasting over mobile and wireless networks Traditionally, cross-layer and joint source-channel coding were seen as incompatible with classically structured networks but recent advances in theory changed this situation.

Combine popular machine learning techniques to create ensemble models using PythonKey FeaturesImplement ensemble models using algorithms such as random forests and AdaBoostApply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model Explore real-world data sets and practical examples coded in scikit-learn and KerasBook DescriptionEnsembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power.

Superlubricity - the state between sliding systems where friction is reduced to almost immeasurable amounts - holds great potential for improving both the economic and environmental credentials of moving mechanical systems.

Build a solid foundation in algorithmic trading by developing, testing and executing powerful trading strategies with real market data using PythonKey FeaturesBuild a strong foundation in algorithmic trading by becoming well-versed with the basics of financial marketsDemystify jargon related to understanding and placing multiple types of trading ordersDevise trading strategies and increase your odds of making a profit without human interventionBook DescriptionIf you want to find out how you can build a solid foundation in algorithmic trading using Python, this cookbook is here to help.

Supervised and unsupervised machine learning made easy in Scala with this quick-start guide.

Big data processing and analytics at speed and scale using command line tools.

Create dynamic dashboards to perform interactive analytics for business intelligence operationsKey FeaturesExplore newly added features in Qlik SenseDiscover best practices to work with data using Qlik SenseLearn to implement advanced functions for better data insightBook DescriptionQlik Sense allows you to explore simple and complex data to reveal hidden insight and data relationships that help you make quality decisions for overall productivity.

Collected in this volume are most of the important theorems and algorithms currently known for planar graphs, together with constructive proofs for the theorems.

Control Systems Design of Bio-Robotics and Bio-Mechatronics with Advanced Applications delivers essential and advanced bioengineering information on the application of control and robotics technologies in the life sciences.

Generalized Functions: Theory and Technique

During the past decade there has been a remarkable growth of interest in problems of systems optimization and of optimal control.

This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and ClusteringKey FeaturesYour handy guide to building machine learning workflows in Go for real-world scenariosBuild predictive models using the popular supervised and unsupervised machine learning techniquesLearn all about deployment strategies and take your ML application from prototype to production readyBook DescriptionMachine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology.

Create and unleash the power of neural networks by implementing C# and .

Stochastic Models: Estimation and Control: v.

Graph Theory in Modern Engineering: Computer Aided Design, Control, Optimization, Reliability Analysis

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Methods of Matrix Algebra

Given modern society's need to control its ever-increasing body of information, digital libraries will be among the most important and influential institutions of this century.

An Algebraic Approach to Non-Classical Logics

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning.

Provability, Computability and Reflection

Decision and Control in Uncertain Resource Systems

Get up to speed with Apache Drill, an extensible distributed SQL query engine that reads massive datasets in many popular file formats such as Parquet, JSON, and CSV.

Perform advanced data manipulation tasks using pandas and become an expert data analyst.

This Handbook documents the main trends in current research between logic and language, including its broader influence in computer science, linguistic theory and cognitive science.

Implement decentralized blockchain applications to build scalable DappsKey FeaturesUnderstand the blockchain ecosystem and its terminologiesImplement smart contracts, wallets, and consensus protocolsDesign and develop decentralized applications using Bitcoin, Ethereum, and HyperledgerBook DescriptionThe Blockchain is a revolution promising a new world without middlemen.

Sequential Methods in Pattern Recognition and Machine Learning

Global biogeochemical cycles

Non-Classical Logics, Model Theory, And Computability

From school lunchrooms to the White House press room, video games are an integral part of our popular culture, and the industry behind them touches all aspects of our lives, gamer and non-gamer alike.

Provability, Computability and Reflection

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.

Combinatorial Set Theory