Machine learning.
概要
| 作品: | 930 作品在 719 項出版品 719 種語言 | |
|---|---|---|
書目資訊
Learning algorithms : = theory and applications in signal processing, control, and communications /
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Machine Learning Models and Algorithms for Big Data Classification = Thinking with Examples for Effective Learning /
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Learning with kernels : = support vector machines, regularization, optimization, and beyond /
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Learning and coordination : = enhancing agent performance through distributed decision making /
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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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Scalable optimization via probabilistic modeling : = from algorithms to applications /
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The cross-entropy method : = a unified approach to combinatorial optimization, Monte-Carlo simulation, and machine learning /
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Learning with kernels = support vector machines, regularization, optimization, and beyond /
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Machine Learning Techniques for Multimedia = Case Studies on Organization and Retrieval /
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Machine learning techniques for multimedia : = case studies on organization and retrieval /
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Advances in machine learning I = dedicated to the memory of Professor Ryszard S. Michalski /
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Advances in machine learning II = dedicated to the memory of Professor Ryszard S. Michalski /
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Handbook of research on machine learning applications and trends = algorithms, methods, and techniques /
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Computational modeling and simulation of intellect = current state and future perspectives /
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Machine learning techniques for adaptive multimedia retrieval = technologies, applications, and perspectives /
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Biomedical image analysis and machine learning technologies = applications and techniques /
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Kernel-based data fusion for machine learning = methods and applications in bioinformatics and text mining /
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Machine learning for spatial environmental data : = theory, applications and software /
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Machine learning algorithms for problem solving in computational applications = intelligent techniques /
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Data-driven methods for adaptive spoken dialogue systems = computational learning for conversational interfaces /
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Probability for statistics and machine learning : = fundamentals and advanced topics /
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Multidimensional particle swarm optimization for machine learning and pattern recognition
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Analysis and design of machine learning techniques = evolutionary solutions for regression, prediction, and control problems /
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Efficient learning machines = theories, concepts, and applications for engineers and system Designers /
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Conformal prediction for reliable machine learning = theory, adaptations, and applications /
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Chemoinformatics and advanced machine learning perspectives = complex computational methods and collaborative techniques /
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Medical image recognition, segmentation and parsing : = machine learning and multiple object approaches /
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Deep learning with applications using Python = chatbots and face, object, and speech recognition with TensorFlow and Keras /
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Optimizing data-to-learning-to-action = the modern approach to continuous performance improvement for businesses /
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Advanced data analytics using Python = with machine learning, deep learning and NLP examples /
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Cognitive computing recipes = artificial intelligence solutions using Microsoft cognitive services and Tensorflow /
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Deep learning for autonomous vehicle control : = algorithms, state-of-the-art, and future prospects /
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Machine learning in computer-aided diagnosis = medical imaging intelligence and analysis /
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Machine learning techniques for gait biometric recognition = using the ground reaction force /
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Machine learning paradigms = artificial immune systems and their applications in software personalization /
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Mastering TensorFlow 1.x : = advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras /
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Practical convolutional neural networks : = implement advanced deep learning models using Python /
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Practical computer vision : = extract insightful information from images using TensorFlow, Keras, and OpenCV /
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Introduction to deep learning using R = a 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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Python machine learning : = machine learning and deep learning with Python, scikit-learn, and TensorFlow /
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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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TensorFlow 1.x deep learning cookbook : = over 90 unique recipes to solve artificial-intelligence driven problems with Python /
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Pro deep learning with TensorFlow = a mathematical approach to advanced artificial intelligence in Python /
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Machine learning for decision makers = cognitive computing fundamentals for better decision making /
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Practical machine learning with Python = a problem-solver's guide to building real-world intelligent systems /
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Oracle business intelligence with machine learning = artificial intelligence techniques in OBIEE for actionable BI /
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Image quality assessment of computer-generated images = based on machine learning and soft computing /
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Targeted learning in data science = causal inference for complex longitudinal studies /
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Electrocardiogram signal classification and machine learning = emerging research and opportunities /
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Enhancing software fault prediction with machine learning = emerging research and opportunities /
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Application of FPGA to real-time machine learning = hardware reservoir computers and software image processing /
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Practical artificial intelligence = machine learning, bots, and agent solutions using C# /
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Deep learning with PyTorch : = a practical approach to building neural network models using PyTorch /
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Cognitively inspired natural language processing = an investigation based on eye-tracking /
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Applied analytics through case studies using SAS and R = implementing predictive models and machine learning techniques /
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Applying machine learning for automated classification of biomedical data in subject-independent settings
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Learn R for applied statistics = with data visualizations, regressions, and statistics /
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Memetic computation = the mainspring of knowledge transfer in a data-driven optimization era /
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Learn Keras for deep neural networks = a fast-track approach to modern deep learning with Python /
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Machine learning applications using Python = cases studies from healthcare, retail, and finance /
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Dynamics on and of complex networks III = machine learning and statistical physics approaches /
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Machine Learning with Microsoft technologies = selecting the right architecture and tools for your project /
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Smart agents for the Industry 4.0 = enabling machine learning in industrial production /
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Deep learning and convolutional neural networks for medical imaging and clinical informatics
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Building machine learning and deep learning models on Google Cloud Platform = a comprehensive guide for beginners /
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Learn TensorFlow 2.0 = implement machine learning and deep learning models with Python /
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Deep learning projects using TensorFlow 2 = neural network development with Python and Keras /
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TensorFlow 2.x in the Colaboratory Cloud = an introduction to deep learning on Google's Cloud Service /
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Advanced machine learning approaches in cancer prognosis = challenges and applications /
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Machine learning concepts with Python and the Jupyter Notebook environment = using Tensorflow 2.0 /
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Supervised learning with Python = concepts and practical implementation using Python /
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Machine learning for Oracle database professionals = deploying model-driven applications and automation pipelines /
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Advanced forecasting with Python = with state-of-the-art-models including LSTMs, Facebook's Prophet, and Amazon's DeepAR /
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State-of-the-art deep learning models in Tensorflow = modern machine learning in the Google colab ecosystem /
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Knowledge transfer between computer vision and text mining = similarity-based learning approaches /
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From curve fitting to machine learning = an illustrative guide to scientific data analysis and computational intelligence /
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Machine learning paradigms = applications of learning and analytics in intelligent systems /
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Machine learning-based natural scene recognition for mobile robot localization in an unknown environment
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Hands-on Scikit-Learn for machine learning applications = data science fundamentals with Python /
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Industrial machine learning = using artificial intelligence as a transformational disruptor /
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Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications
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Modern approaches in machine learning and cognitive science = a walkthrough : latest trends in AI /
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Machine learning in team sports = performance analysis and talent identification in beach soccer and sepak-takraw /
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Advances in cybernetics, cognition, and machine learning for communication technologies
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Artificial intelligence and machine learning for digital pathology = state-of-the-art and future challenges /
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Learn data mining through Excel = a step-by-step approach for understanding machine learning methods /
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Machine learning paradigms = advances in deep learning-based technological applications /
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Images as data for social science research : = an introduction to convolutional neural nets for image classification /
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Kernel based algorithms for mining huge data sets : = supervised, semi-supervised, and unsupervised learning /
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Machine learning for audio, image and video analysis : = theory and applications /
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Machine learning in social networks = embedding nodes, edges, communities, and graphs /
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Artificial neural networks with TensorFlow 2 = ANN architecture machine learning projects /
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Practical machine learning with AWS = process, build, deploy, and productionize your models using AWS /
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Machine learning for economics and finance in TensorFlow 2 = deep learning models for research and industry /
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Hyperparameter optimization in machine learning = make your machine learning and deep learning models more efficient /
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Practical natural language processing with Python = with case studies from industries using text data at scale /
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Proceedings of the Second International Conference on Information Management and Machine Intelligence = ICIMMI 2020 /
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Hands-on question answering systems with BERT = applications in neural networks and natural language processing /
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Data Science and Internet of Things = research and applications at the intersection of DS and IoT /
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AI and machine learning paradigms for health monitoring system = intelligent data analytics /
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Development and future of internet of drones (IoD) = insights, trends and road ahead /
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Computer vision with maker tech = detecting people with a Raspberry Pi, a thermal camera, and machine learning /
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Data science revealed = with feature engineering, data visualization, pipeline development, and hyperparameter tuning /
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The application of artificial intelligence = step-by-step guide from beginner to expert /
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Fluctuation-induced network control and learning = applying the Yuragi principle of brain and biological systems /
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Machine learning and data mining for emerging trend in cyber dynamics = theories and applications /
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Quantum machine learning with Python = using Cirq from Google Research and IBM Qiskit /
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Beginning machine learning in the browser = quick-start guide to gait analysis with JavaScript and TensorFlow.js /
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Modern approaches in machine learning and cognitive science = a walkthrough : latest trends in AI.. Volume 2 /
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Practical machine learning for streaming data with Python = design, develop, and validate online learning models /
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Deep learning with Python = learn best practices of deep learning models with PyTorch /
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Hardware-aware probabilistic machine learning models = learning, inference and use cases /
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How algorithms create and prevent fake news = exploring the impacts of social media, deepfakes, GPT-3, and more /
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Efficacy analysis in clinical trials an update = efficacy analysis in an era of machine learning /
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Centrality and diversity in search = roles in A.I., machine learning, social networks, and pattern recognition /
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Adversarial and uncertain reasoning for adaptive cyber defense = control- and game-theoretic approaches to cyber security /
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Active lighting and its application for computer vision = 40 years of history of active lighting techniques /
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Machine learning methods for stylometry = authorship attribution and author profiling /
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Introducing HR analytics with machine learning = empowering practitioners, psychologists, and organizations /
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Android malware detection using machine learning = data-driven fingerprinting and threat intelligence /
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Machine learning in elite volleyball = integrating performance analysis, competition and training strategies /
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Handbook of research on disease prediction through data analytics and machine learning
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Artificial Intelligence and machine learning applications in civil, mechanical, and industrial engineering
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Introduction to deep learning for engineers = using python and google cloud platform /
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The lognormality principle and its applications in e-security, e-learning and e-health
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Natural language processing recipes = unlocking text data with machine learning and deep learning using Python /
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Applied machine learning for health and fitness = a practical guide to machine learning with deep vision, sensors and IoT /
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Intelligent feature selection for machine learning using the dynamic wavelet fingerprint
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3D point cloud analysis = traditional, deep learning, and explainable machine learning methods /
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Beginning deep learning with TensorFlow = work with Keras, MNIST data sets, and advanced neural networks /
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Hands-on machine learning with Python = implement neural network solutions with Scikit-learn and PyTorch /
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Adaptive machine learning algorithms with Python = solve data analytics and machine learning problems on edge devices /
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Context-aware machine learning and mobile data analytics = automated rule-based services with intelligent decision-making /
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Data analysis with machine learning for psychologists = crash course to learn Python 3 and machine learning in 10 hours /
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Large-scale structure of the universe = cosmological simulations and machine learning /
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Artificial intelligence and machine learning for healthcare.. Vol. 2,. Emerging methodologies and trends
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Machine learning and big data analytics paradigms = analysis, applications and challenges /
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Beginning MLOps with MLFlow = deploy models in AWS SageMaker, Google Cloud, and Microsoft Azure /
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Deploy machine learning models to production = with Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform /
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Deep learning on Windows = building deep learning computer vision systems on Microsoft Windows /
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Applied data science using Pyspark = learn the end-to-end predictive model-building cycle /
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The digital journey of banking and insurance.. Volume II,. Digitalization and machine learning
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Computer vision - ECCV 2020 = 16th European Conference, Glasgow, UK, August 23-28, 2020 : proceedings.. Part IV /
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Computer vision - ECCV 2020 = 16th European Conference, Glasgow, UK, August 23-28, 2020 : proceedings.. Part V /
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Data analysis for direct numerical simulations of turbulent combustion = from equation-based analysis to machine learning /
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IoT machine learning applications in telecom, energy, and agriculture = with Raspberry Pi and Arduino Using Python /
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On the path to AI = Law's prophecies and the conceptual foundations of the machine learning age /
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Beginning Apache Spark 3 = with DataFrame, Spark SQL, structured streaming, and spark machine learning library /
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Artificial intelligence with Power BI : = take your data analytics skills to the next level by leveraging the AI capabilities in Power BI /
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Machine learning on geographical data using Python : = introduction into geodata with applications and use cases /
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Machine learning, deep learning, big data, and internet of things for healthcare /
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Computational biology and machine learning for metabolic engineering and synthetic biology /
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Application of soft computing, machine learning, deep learning and optimizations in geoengineering and geoscience
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Human perception of visual information = psychological and computational perspectives /
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Machine learning for critical internet of medical things = applications and use cases /
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Building an effective data science practice = a framework to bootstrap and manage a successful data science practice /
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Data science solutions with Python = fast and scalable models using Keras, Pyspark Mllib, H2O, XGBoost, and scikit-Learn /
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Practical AI for healthcare professionals = machine learning with Numpy, Scikit-learn, and TensorFlow /
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On spatio-temporal data modelling and uncertainty quantification using machine learning and information theory
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Recent advances in internet of things and machine learning = real-world applications /
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AI for disease surveillance and pandemic intelligence = intelligent disease detection in action /
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A few things I know about her = a personally machine learning inspired approach to understand surrounding nature /
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Machine learning for auditors = automating fraud investigations through artificial intelligence /
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Applied deep learning with TensorFlow 2 = learn to implement advanced deep learning techniques with Python /
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Artificial intelligence and machine learning in public healthcare = opportunities and societal impact /
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Productive and efficient data science with Python = with modularizing, memory profiles, and parallel/GPU processing /
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Machine learning for computer scientists and data analysts = from an applied perspective /
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Machine learning on geographical data using Python = introduction into geodata with applications and use cases /
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Machine learning and data analytics for solving business problems = methods, applications, and case studies /
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Synthetic data for deep learning = generate synthetic data for decision making and applications with Python and R /
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Deep learning for data analytics = foundations, biomedical applications, and challenges /
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Images as data for social science research = an introduction to convolutional neural nets for image classification /
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Deep learning for EEG-based brain-computer interfaces = representations, algorithms and applications /
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Recurrent neural networks for prediction : = learning algorithms, architectures, and stability /
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Handbook of research on machine learning techniques for pattern recognition and information security
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Beginner's guide to Streamlit with Python = build web-based data and machine learning applications /
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Modern deep learning for tabular data = novel approaches to common modeling problems /
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Pro deep learning with TensorFlow 2.0 = a mathematical approach to advanced artificial intelligence in Python /
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Machine learning empowered intelligent data center networking = evolution, challenges and opportunities /
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Machine learning approaches for evaluating statistical information in the agricultural sector
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Machine learning and data sciences for financial markets = a guide to contemporary practices /
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Linear algebra and optimization with applications to machine learning.. Volume II,. Fundamentals of optimization theory with applications to machine learning /
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The Effectiveness of Digital Forensics and Security Strategies in Using AI and Machine Learning to Protect Children Online.
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Applied Recommender Systems with Python = Build Recommender Systems with Deep Learning, NLP and Graph-Based Techniques /
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PyTorch recipes = a problem-solution approach to build, train and deploy neural network models /
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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 learning = algorithms, product modeling, and applications /
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Precision health and artificial intelligence = with privacy, ethics, bias, health equity, best practices, and case studies /
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Essentials of Excel VBA, Python, and R.. Volume II,. Financial derivatives, risk management and machine learning
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Robust and multivariate statistical methods = festschrift in honor of David E. Tyler /
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Optimization and machine learning : = optimization for machine learning and machine learning for optimization /
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Shallow and deep learning principles = scientific, philosophical, and logical perspectives /
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AI-ML for decision and risk analysis = challenges and opportunities for normative decision theory /
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Machine learning for data science handbook = data mining and knowledge discovery handbook /
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Hybrid competitive learning method using the fireworks algorithm and artificial neural networks
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Distributed machine learning with Pyspark = migrating effortlessly from Pandas and Scikit-Learn /
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A practical approach for machine learning and deep learning algorithms = tools and techniques using MATLAB and Python /
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Machine learning for beginners = learn to build machine learning systems using Python /
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Machine learning cookbook with Python = create ML and data analytics projects using some amazing open datasets /
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Practical time series analysis : = prediction with statistics and machine learning /
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Rank-based methods for shrinkage and selection : = with application to machine learning /
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Deep learning in smart eHealth systems = evaluation leveraging for Parkinson's disease /
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Embedded machine learning for cyber-physical, iot, and edge computing = hardware architectures /
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Embedded machine learning for cyber-physical, IoT, and edge computing = use cases and emerging challenges /
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Quantum machine learning = thinking and exploration in neural network models for quantum science and quantum computing /
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Machine learning for decision makers = cognitive computing fundamentals for better decision making /
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(書目-電子資源)
Modern approaches in machine learning and cognitive science = a walkthrough.. Volume 4 /
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Machine learning applications for intelligent energy management = invited chapters from experts on the energy field /
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Data mining and machine learning in sports = success metrics for elite goalkeepers in European football leagues /
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(書目-電子資源)
Recent advances in logo detection using machine learning paradigms = theory and practice /
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Machine learning for materials discovery = numerical recipes and practical applications /
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Reduction, approximation, machine learning, surrogates, emulators and simulators = RAMSES /
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(書目-電子資源)
MLOps with Ray = best practices and strategies for adopting machine learning operations /
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(書目-電子資源)
Deep learning = theory, architectures and applications in speech, image and language processing /
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(書目-電子資源)
Artificial intelligence and machine learning with R = applications in the field of business analytics /
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Sustainable IoT and data analytics enabled machine learning techniques and applications
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(書目-電子資源)
Geometric algebra applications.. Vol. III,. Integral transforms, machine learning, and quantum computing
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Large language models projects = apply and implement strategies for large language models /
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Data engineering for machine learning pipelines = from Python libraries to ML pipelines and cloud platforms /
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Machine learning and data science techniques for effective government service delivery
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Applying machine learning techniques to bioinformatics = few-shot and zero-shot methods /
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Advanced interdisciplinary applications of machine learning Python libraries for data science
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(書目-電子資源)
Shallow learning vs. deep learning = a practical guide for machine learning solutions /
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Recent advances in machine learning techniques and sensor applications for human emotion, activity recognition and support
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