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Transactional Machine Learning with Data Streams and AutoML

data streams with auto machine learning integrated with Apache Kafka. Transactional Machine Learning with Data Streams and AutoML;

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Hands-On Automated Machine Learning

Automate data and model pipelines for faster machine learning applications Key Features Build automated modules for different machine;

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Automated Machine Learning

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key;

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Metalearning: Applications to Automated Machine Learning and Data Mining

adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, AutoML is concerned with;

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Automated Machine Learning and Meta-Learning for Multimedia

practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data;

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Automated Machine Learning for Business

, Automated Machine Learning for Business provides a clear overview of the process and engages with essential tools for the future of data science.;

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Automated Machine Learning for Business

, Automated Machine Learning for Business provides a clear overview of the process and engages with essential tools for the future of data science.;

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IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning

Streams 2020, and First International Workshop on IoT, Edge, and Mobile for Embedded Machine Learning, ITEM 2020, co-located with ECML/PKDD 2020;

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Deep Learning with TensorFlow 2 and Keras

apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learn Build machine learning and deep;

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Machine Learning Engineering with MLflow

MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to;

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Learn Amazon SageMaker

. What you will learn Become well-versed with data annotation and preparation techniques Use AutoML features to build and train machine;

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Introducing Machine Learning

Introducing Machine Learning delivers practical knowledge to make the most of them. Dino and Francesco Esposito start with a quick overview of the;

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Web and Big Data

mining and data streams; query processing; topic modeling; machine learning; recommendation systems; distributed data processing and applications;

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Web and Big Data

mining and data streams; query processing; topic modeling; machine learning; recommendation systems; distributed data processing and applications;

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Learn Amazon SageMaker

Amazon Web Services (AWS) * Become well-versed with data annotation and preparation techniques * Use AutoML features to build and train machine;

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Spark in Action

Working with big data can be complex and challenging, in part because of the multiple analysis frameworks and tools required;

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Automated Design of Machine Learning and Search Algorithms

This book presents recent advances in automated machine learning (AutoML) and automated algorithm design and indicates the future;

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Data Science on AWS

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on;

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Data Science with Python

Leverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and;

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Machine Learning with SAP

Work smarter with machine learning! Begin with core machine learning concepts-types of learning, algorithms, data preparation, and more;

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Mastering PyTorch

Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use;

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Machine Learning and Knowledge Discovery in Databases

learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data;

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Machine Learning and Knowledge Discovery in Databases

; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix;

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Machine Learning and Knowledge Discovery in Databases

; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix;

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Emerging Paradigms in Machine Learning

challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non;

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Data Classification

areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way;

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Data Classification

areas as pattern recognition, database, data mining, and machine learning. Addressing the work of these different communities in a unified way;

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