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.
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.
Build, design and improve advanced business intelligence solutions using Tableau's latest features, including Tableau Prep, Tableau Hyper, and Tableau ServerKey FeaturesMaster new features in Tableau 2019.
Build efficient, high-performance & scalable systems to process large volumes of data with Apache IgniteKey FeaturesUnderstand Apache Ignite's in-memory technologyCreate High-Performance app components with IgniteBuild a real-time data streaming and complex event processing systemBook DescriptionApache Ignite is a distributed in-memory platform designed to scale and process large volume of data.
A step-by-step approach to building stunning dashboards with QlikViewKey FeaturesPerform effective storytelling through interactive dashboards built with QlikViewCreate different types of visualizations from a variety of data sourcesIncludes tips, tricks, and best practices to perform effective Business Intelligence using QlikViewBook DescriptionQlikView is one of the market leaders when it comes to building effective Business Intelligence solutions.
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problemsKey FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook DescriptionMachine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners.
Explore Kinesis managed services such as Kinesis Data Streams, Kinesis Data Analytics, Kinesis Data Firehose, and Kinesis Video Streams with the help of practical use casesKey FeaturesGet well versed with the capabilities of Amazon KinesisExplore the monitoring, scaling, security, and deployment patterns of various Amazon Kinesis servicesLearn how other Amazon Web Services and third-party applications such as Splunk can be used as destinations for Kinesis dataBook DescriptionAmazon Kinesis is a collection of secure, serverless, durable, and highly available purpose-built data streaming services.
Data Analysis Methods in Physical Oceanography is a practical referenceguide to established and modern data analysis techniques in earth and oceansciences.
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.
Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and moreKey FeaturesMaster machine learning, deep learning, and predictive modeling concepts in R 3.
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.
Get up and running with machine learning life cycle management and implement MLOps in your organizationKey FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook DescriptionEngineering MLps presents comprehensive insights into MLOps coupled with real-world examples in Azure to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.
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.
Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges.
Gain a working knowledge of advanced machine learning and explore Python's powerful tools for extracting data from images and videosKey FeaturesImplement image classification and object detection using machine learning and deep learningPerform image classification, object detection, image segmentation, and other Computer Vision tasksCrisp content with a practical approach to solving real-world problems in Computer VisionBook DescriptionPython is the ideal programming language for rapidly prototyping and developing production-grade codes for image processing and Computer Vision with its robust syntax and wealth of powerful libraries.
Optimal control theory of distributed parameter systems has been a very active field in recent years; however, very few books have been devoted to the studiy of computational algorithms for solving optimal control problems.
Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learningKey FeaturesGet up and running with graph analytics with the help of real-world examplesExplore various use cases such as fraud detection, graph-based search, and recommendation systemsGet to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scalingBook DescriptionNeo4j is a graph database that includes plugins to run complex graph algorithms.
Get the most out of Elasticsearch 7's new features to build, deploy, and manage efficient applicationsKey FeaturesDiscover the new features introduced in Elasticsearch 7Explore techniques for distributed search, indexing, and clusteringGain hands-on knowledge of implementing Elasticsearch for your enterpriseBook DescriptionElasticsearch is one of the most popular tools for distributed search and analytics.
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.
This book deals with measures of information (the most important ones being called entropies), their properties, and, reciprocally, with questions concerning which of these properties determine known measures of information, and which are the most general formulas satisfying reasonable requirements on practical measures of information.
While most textbooks on Numerical Analysis discuss linear techniques for the solution of various numerical problems, this book introduces and illustrates nonlinear methods.
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.
