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This book explores break-through approaches to tackling and mitigating the well-known problems of compiler optimization using design space;
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scheduled control. PID Control System Design and Automatic Tuning using MATLAB/Simulink introduces PID control system structures, sensitivity;
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features detection; automatic MRI image segmentation for brain tumor detection using the multi-level sigmoid activation function; and computer;
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Automated Machine Learning using Azure Explore aspects such as algorithm selection, auto featurization, and hyperparameter tuning Understand how;
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Build automatic classification and prediction models using unsupervised learning About This Book * Harness the ability to build;
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Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using;
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. You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real;
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platforms. You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning;
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explore the features of cloud AutoML platforms by building machine learning models using AutoML. The book will also show you how to develop;
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Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy;
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This book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation;
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learning development and frameworks Assess model diagnosis and tuning in machine learning Examine text mining, natuarl language processing (NLP;
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frameworks Assess model diagnosis and tuning in machine learning Examine text mining, natuarl language processing (NLP), and recommender systems;
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Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML;
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near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready;
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. This book shows that the problem of tuning a me- heuristic can be described and solved as a machine learning problem. Using the machine;
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apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to;
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of the different aspects of automated Machine Learning, and you'll be able to incorporate automation tasks using practical datasets. You can;
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supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit;
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, automatic performance tuning, transactional memory, and emerging applications. As computing power increasingly comes from parallelism, software;
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, automatic performance tuning, transactional memory, and emerging applications. As computing power increasingly comes from parallelism, software;
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automatic tuning method is demonstrated using several examples for benchmark process models recurring frequently in many industry applications.;
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Machine Learning What are the Python Keywords? How to make predictions using;
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research on the automatic assessment of parkinsonian speech from the point of view of such disciplines as machine learning, speech technology;
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neural networks, GANs, and other models Discover best practices for evaluating and tuning models Predict continuous target outcomes using;
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Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and;
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This book presents a unique, understandable view of machine learning using many practical examples and access to free professional software;
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