Machine learning.
Overview
| Works: | 78 works in 77 publications in 77 languages | |
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Titles
Genetic algorithms in search, optimization, and machine learning /
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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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Machine learning techniques for adaptive multimedia retrieval :technologies, applications, and perspectives /
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Machine learning and knowledge discovery for engineering systems health management /
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Machine interpretation of patterns :image analysis and data mining /
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Recurrent neural networks for predictionlearning algorithms, architectures, and stability /
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Learning with kernelssupport vector machines, regularization, optimization, and beyond /
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Intelligence emerging :adaptivity and search in evolving neural systems /
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Machine learning for audio, image and video analysis :theory and applications /
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Exploiting the power of group differences :using patterns to solve data analysis problems /
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Reasoning with probabilistic and deterministic graphical models :exact algorithms /
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Machine learning with Spark and Python :essential techniques for predictive analytics /
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Deep learning illustrated :a visual, interactive guide to artificial intelligence /
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Hands-On Machine Learning with ML.NET :getting started with Microsoft ML.NET to implement popular machine learning algorithms in C# /
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Deep learning for autonomous vehicle control :algorithms, state-of-the-art, and future prospects /
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Unsettled Technology Opportunities for Vehicle Health Management and the Role for Health-Ready Components.
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AI and machine learning for coders :a programmer's guide to artificial intelligence /
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Deep learning projects using TensorFlow 2 :neural network development with Python and Keras /
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AI for computer architecture :principles, practice, and prospects /
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Deep learning with Swift for TensorFlow :differentiable programming with Swift /
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Head and neck tumor segmentation :First Challenge, HECKTOR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, proceedings /
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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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Guide to deep learning basics :logical, historical and philosophical perspectives /
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Getting started with Amazon SageMaker Studio :learn to build end-to-end machine learning projects in the SageMaker machine learning IDE /
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Adaptive machine learning algorithms with Python :solve data analytics and machine learning problems on edge devices /
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Hands-on machine learning with Python :implement neural network solutions with Scikit-learn and PyTorch /
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Practical AI for healthcare professionals :machine learning with Numpy, Scikit-learn, and TensorFlow /
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The TensorFlow Workshop :a hands-on guide to building deep learning models from scratch using real-world datasets /
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Beginning with deep learning using TensorFlow :a beginners guide to TensorFlow and keras for practicing deep learning principles and applications /
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Hands-on system design :learn system design, scaling applications, software development design patterns with real use-cases /
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Introduction to IoT with machine learning and image processing using Raspberry Pi /
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Machine learning for financial risk management with Python :algorithms for modeling risk /
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Reproducible data science with Pachyderm :learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0 /
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Text as data :a new framework for machine learning and the social sciences /
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Machine learning for auditors :automating fraud investigations through artificial intelligence /
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Combining DataOps, MLOps and DevOps :outperform analytics and software development with expert practices on process optimization and automation /
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Distributed machine learning with Python :accelerating model training and serving with distributed systems /
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Hardware-aware probabilistic machine learning models :learning, inference and use cases /
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Machine learning with Dynamics 365 and Power Platform :the ultimate guide to apply predictive analytics /
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A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence
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