Machine learning.
概要
| 作品: | 850 作品在 673 項出版品 673 種語言 | |
|---|---|---|
書目資訊
Geometric computing for perception action systems :concepts, algorithms, and scientific applications /
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Recurrent neural networks for prediction :learning algorithms, architectures, and stability /
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Adaptive blind signal and image processing :learning algorithms and applications /
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Learning with kernels :support vector machines, regularization, optimization, and beyond /
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An introduction to support vector machines :and other kernel-based learning methods /
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Recurrent neural networks for prediction :learning algorithms, architectures, and stability /
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Kernel based algorithms for mining huge data sets :supervised, semi-supervised, and unsupervised learning /
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Machine learning and data mining for computer security :methods and applications /
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Machine Learning Techniques for Multimedia :Case Studies on Organization and Retrieval /
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Learning with kernels :support vector machines, regularization, optimization, and beyond /
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Machine learning methods for commonsense reasoning processes :interactive models /
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Advances in machine learning Idedicated to the memory of Professor Ryszard S. Michalski /
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Advances in machine learning IIdedicated to the memory of Professor Ryszard S. Michalski /
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Machine learning for spatial environmental data :theory, applications and software /
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Advanced mapping of environmental data :geostatistics, machine learning, and Bayesian maximum entropy /
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Machine learning methods in the environmental sciences :neural networks and kernels /
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Kernel-based data fusion for machine learningmethods and applications in bioinformatics and text mining /
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Machine learning techniques for adaptive multimedia retrieval :technologies, applications, and perspectives /
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Handbook of research on machine learning applications and trendsalgorithms, methods, and techniques /
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Computational modeling and simulation of intellectcurrent state and future perspectives /
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Machine learning techniques for adaptive multimedia retrievaltechnologies, applications, and perspectives /
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Biomedical image analysis and machine learning technologiesapplications and techniques /
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Machine learning algorithms for problem solving in computational applicationsintelligent techniques /
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Machine learning in computer-aided diagnosismedical imaging intelligence and analysis /
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Data-driven methods for adaptive spoken dialogue systemscomputational learning for conversational interfaces /
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Reasoning with probabilistic and deterministic graphical models :exact algorithms /
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Multidimensional particle swarm optimization for machine learning and pattern recognition
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Analysis and design of machine learning techniquesevolutionary solutions for regression, prediction, and control problems /
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Efficient learning machinestheories, concepts, and applications for engineers and system Designers /
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Conformal prediction for reliable machine learningtheory, adaptations, and applications /
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Machine Learning Models and Algorithms for Big Data ClassificationThinking with Examples for Effective Learning /
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Machine learning techniques for gait biometric recognitionusing the ground reaction force /
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Knowledge transfer between computer vision and text miningsimilarity-based learning approaches /
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From curve fitting to machine learningan illustrative guide to scientific data analysis and computational intelligence /
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Machine learning paradigmsartificial immune systems and their applications in software personalization /
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Medical image recognition, segmentation and parsingmachine learning and multiple object approaches /
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MATLAB deep learningwith machine learning, neural networks and artificial intelligence /
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Introduction to deep learning using Ra step-by-step guide to learning and implementing deep learning models using R /
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Game-theoretic learning and distributed optimization in memoryless multi-agent systems
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Machine learning algorithms :reference guide for popular algorithms for data science and machine learning /
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Python machine learning :machine learning and deep learning with Python, scikit-learn, and TensorFlow /
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Pro deep learning with TensorFlowa mathematical approach to advanced artificial intelligence in Python /
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Machine learning for decision makerscognitive computing fundamentals for better decision making /
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Practical machine learning with Pythona problem-solver's guide to building real-world intelligent systems /
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Oracle business intelligence with machine learningartificial intelligence techniques in OBIEE for actionable BI /
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Electrocardiogram signal classification and machine learningemerging research and opportunities /
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Enhancing software fault prediction with machine learningemerging research and opportunities /
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Image quality assessment of computer-generated imagesbased on machine learning and soft computing /
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Deep learning with applications using Pythonchatbots and face, object, and speech recognition with TensorFlow and Keras /
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Optimizing data-to-learning-to-actionthe modern approach to continuous performance improvement for businesses /
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Advanced data analytics using Pythonwith machine learning, deep learning and NLP examples /
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Application of FPGA to real-time machine learninghardware reservoir computers and software image processing /
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Practical artificial intelligencemachine learning, bots, and agent solutions using C# /
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Evolutionary approach to machine learning and deep neural networksneuro-evolution and gene regulatory networks /
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Pro machine learning algorithmsa hands-on approach to implementing algorithms in Python and R /
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Cognitively inspired natural language processingan investigation based on eye-tracking /
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Applied analytics through case studies using SAS and Rimplementing predictive models and machine learning techniques /
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IoT, AI, and blockchain for .NETbuilding a next-generation application from the ground up /
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Practical Java machine learningprojects with Google Cloud platform and Amazon web services /
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Distributed optimization and statistical learning via the alternating direction method of multipliers /
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Hands-on machine learning with Scikit-Learn and TensorFlow :concepts, tools, and techniques to build intelligent systems /
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Applying machine learning for automated classification of biomedical data in subject-independent settings
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Cognitive computing recipesartificial intelligence solutions using Microsoft cognitive services and Tensorflow /
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Memetic computationthe mainspring of knowledge transfer in a data-driven optimization era /
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Learn Keras for deep neural networksa fast-track approach to modern deep learning with Python /
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Machine learning applications using Pythoncases studies from healthcare, retail, and finance /
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Dynamics on and of complex networks IIImachine learning and statistical physics approaches /
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Machine learning paradigmsapplications of learning and analytics in intelligent systems /
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Machine Learning with Microsoft technologiesselecting the right architecture and tools for your project /
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Building machine learning and deep learning models on Google Cloud Platforma comprehensive guide for beginners /
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Deep learning and convolutional neural networks for medical imaging and clinical informatics
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Statistics for machine learning :techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R /
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Advances in cybernetics, cognition, and machine learning for communication technologies
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Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications
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Hands-on Scikit-Learn for machine learning applicationsdata science fundamentals with Python /
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Industrial machine learningusing artificial intelligence as a transformational disruptor /
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Modern approaches in machine learning and cognitive sciencea walkthrough : latest trends in AI /
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Machine learning in team sportsperformance analysis and talent identification in beach soccer and sepak-takraw /
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Machine learning-based natural scene recognition for mobile robot localization in an unknown environment
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Data analysis for direct numerical simulations of turbulent combustionfrom equation-based analysis to machine learning /
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IoT machine learning applications in telecom, energy, and agriculturewith Raspberry Pi and Arduino Using Python /
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On the path to AILaw's prophecies and the conceptual foundations of the machine learning age /
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Deep learning for autonomous vehicle control :algorithms, state-of-the-art, and future prospects /
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Artificial intelligence and machine learning for digital pathologystate-of-the-art and future challenges /
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Learn data mining through Excela step-by-step approach for understanding machine learning methods /
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Machine learning paradigmsadvances in deep learning-based technological applications /
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Deep learning projects using TensorFlow 2neural network development with Python and Keras /
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Applied machine learning for health and fitnessa practical guide to machine learning with deep vision, sensors and IoT /
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Active lighting and its application for computer vision40 years of history of active lighting techniques /
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Machine learning concepts with Python and the Jupyter Notebook environmentusing Tensorflow 2.0 /
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Efficacy analysis in clinical trials an updateefficacy analysis in an era of machine learning /
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Centrality and diversity in searchroles in A.I., machine learning, social networks, and pattern recognition /
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Adversarial and uncertain reasoning for adaptive cyber defensecontrol- and game-theoretic approaches to cyber security /
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Computer vision - ECCV 202016th European Conference, Glasgow, UK, August 23-28, 2020 : proceedings.Part IV /
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Computer vision - ECCV 202016th European Conference, Glasgow, UK, August 23-28, 2020 : proceedings.Part V /
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Intelligent feature selection for machine learning using the dynamic wavelet fingerprint
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Artificial neural networks with TensorFlow 2ANN architecture machine learning projects /
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Practical machine learning with AWSprocess, build, deploy, and productionize your models using AWS /
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Machine learning for economics and finance in TensorFlow 2deep learning models for research and industry /
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Hyperparameter optimization in machine learningmake your machine learning and deep learning models more efficient /
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Practical natural language processing with Pythonwith case studies from industries using text data at scale /
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Machine learning and big data analytics paradigmsanalysis, applications and challenges /
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Beginning MLOps with MLFlowdeploy models in AWS SageMaker, Google Cloud, and Microsoft Azure /
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Deploy machine learning models to productionwith Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /
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Deep learning on Windowsbuilding deep learning computer vision systems on Microsoft Windows /
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Applied data science using Pysparklearn the end-to-end predictive model-building cycle /
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Proceedings of the Second International Conference on Information Management and Machine IntelligenceICIMMI 2020 /
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Hands-on question answering systems with BERTapplications in neural networks and natural language processing /
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TensorFlow 2.x in the Colaboratory Cloudan introduction to deep learning on Google's Cloud Service /
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Modern approaches in machine learning and cognitive sciencea walkthrough : latest trends in AI.Volume 2 /
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Practical machine learning for streaming data with Pythondesign, develop, and validate online learning models /
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Hardware-aware probabilistic machine learning modelslearning, inference and use cases /
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The application of artificial intelligencestep-by-step guide from beginner to expert /
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Fluctuation-induced network control and learningapplying the Yuragi principle of brain and biological systems /
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Machine learning and data mining for emerging trend in cyber dynamicstheories and applications /
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Beginning machine learning in the browserquick-start guide to gait analysis with JavaScript and TensorFlow.js /
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Hands-on machine learning with ML.NET :getting started with Microsoft's ML.NET to implement popular machine learning algorithms in C# /
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Data Science and Internet of Thingsresearch and applications at the intersection of DS and IoT /
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AI and machine learning paradigms for health monitoring systemintelligent data analytics /
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Computer vision with maker techdetecting people with a Raspberry Pi, a thermal camera, and machine learning /
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Data science revealedwith feature engineering, data visualization, pipeline development, and hyperparameter tuning /
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Introducing HR analytics with machine learningempowering practitioners, psychologists, and organizations /
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Android malware detection using machine learningdata-driven fingerprinting and threat intelligence /
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Machine learning in elite volleyballintegrating performance analysis, competition and training strategies /
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Machine learning for Oracle database professionalsdeploying model-driven applications and automation pipelines /
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Advanced forecasting with Pythonwith state-of-the-art-models including LSTMs, Facebook's Prophet, and Amazon's DeepAR /
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Handbook of research on disease prediction through data analytics and machine learning
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Applications of advanced machine intelligence in computer vision and object recognitionemerging research and opportunities /
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Artificial Intelligence and machine learning applications in civil, mechanical, and industrial engineering
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How algorithms create and prevent fake newsexploring the impacts of social media, deepfakes, GPT-3, and more /
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Quantum machine learningan applied approach : the theory and application of quantum machine learning in science and industry /
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The lognormality principle and its applications in e-security, e-learning and e-health
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State-of-the-art deep learning models in Tensorflowmodern machine learning in the Google colab ecosystem /
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Natural language processing recipesunlocking text data with machine learning and deep learning using Python /
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The digital journey of banking and insurance.Volume II,Digitalization and machine learning
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3D point cloud analysistraditional, deep learning, and explainable machine learning methods /
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Beginning Apache Spark 3with DataFrame, Spark SQL, structured streaming, and spark machine learning library /
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Applied deep learning and computer vision for self-driving cars :build autonomous vehicles using deep neural networks and behavior-cloning techniques /
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Context-aware machine learning and mobile data analyticsautomated rule-based services with intelligent decision-making /
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Artificial intelligence and machine learning in public healthcareopportunities and societal impact /
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Empowering artificial intelligence through machine learning :new advances and applications /
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Application of soft computing, machine learning, deep learning and optimizations in geoengineering and geoscience
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Building an effective data science practicea framework to bootstrap and manage a successful data science practice /
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Data science solutions with Pythonfast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn /
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Practical AI for healthcare professionalsmachine learning with Numpy, Scikit-learn, and TensorFlow /
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Beginning deep learning with TensorFlowwork with Keras, MNIST data sets, and advanced neural networks /
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On spatio-temporal data modelling and uncertainty quantification using machine learning and information theory
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AI for disease surveillance and pandemic intelligenceintelligent disease detection in action /
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A few things I know about hera personally machine learning inspired approach to understand surrounding nature /
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Machine learning for auditorsautomating fraud investigations through artificial intelligence /
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Hands-on machine learning with Pythonimplement neural network solutions with Scikit-learn and PyTorch /
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Adaptive machine learning algorithms with Pythonsolve data analytics and machine learning problems on edge devices /
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Applied deep learning with TensorFlow 2learn to implement advanced deep learning techniques with Python /
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Deep learning for data analyticsfoundations, biomedical applications, and challenges /
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Handbook of research on machine learning techniques for pattern recognition and information security
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Productive and efficient data science with Pythonwith modularizing, memory profiles, and parallel/GPU processing /
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Images as data for social science researchan introduction to convolutional neural nets for image classification /
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Deep learning for EEG-based brain-computer interfacesrepresentations, algorithms and applications /
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Machine learning for computer scientists and data analystsfrom an applied perspective /
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Machine learning on geographical data using Pythonintroduction into geodata with applications and use cases /
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Data analysis with machine learning for psychologistscrash course to learn Python 3 and machine learning in 10 hours /
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Machine learning and data analytics for solving business problemsmethods, applications, and case studies /
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Synthetic data for deep learninggenerate synthetic data for decision making and applications with Python and R /
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Machine learning empowered intelligent data center networkingevolution, challenges and opportunities /
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Advances in non-invasive biomedical signal sensing and processing with machine learning
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Novel financial applications of machine learning and deep learningalgorithms, product modeling, and applications /
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Precision health and artificial intelligencewith privacy, ethics, bias, health equity, best practices, and case studies /
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A practical approach for machine learning and deep learning algorithmstools and techniques using MATLAB and Python /
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Machine learning cookbook with Pythoncreate ML and data analytics projects using some amazing open datasets /
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Essentials of Excel VBA, Python, and R.Volume II,Financial derivatives, risk management and machine learning
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Artificial intelligence and machine learning for healthcare.Vol. 2,Emerging methodologies and trends
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Applied Recommender Systems with PythonBuild Recommender Systems with Deep Learning, NLP and Graph-Based Techniques /
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Advanced data analytics using Pythonwith architectural patterns, text and image classification, and optimization techniques /
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PyTorch recipesa problem-solution approach to build, train and deploy neural network models /
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Beginner's guide to Streamlit with Pythonbuild web-based data and machine learning applications /
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Pro deep learning with TensorFlow 2.0a mathematical approach to advanced artificial intelligence in Python /
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Shallow and deep learning principlesscientific, philosophical, and logical perspectives /
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Machine learning for data science handbookdata mining and knowledge discovery handbook /
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AI-ML for decision and risk analysischallenges and opportunities for normative decision theory /
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