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In this book, some of today's leading neurolinguists and psycholinguists provide insight into the nature of phonological processing using;
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Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more Key Features Apply popular;
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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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Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition;
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selected from 53 submissions. The papers are organized in topical sections on machine translation, speech and speaker recognition, machine learning;
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The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning;
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learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes. The book;
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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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where machine learning has been implemented. Machine Learning Applications Using Python is divided into three sections, one for each of the;
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4.6.1, includes examples on ASP.NET Core 1.0 * Set up your business application to start using machine learning techniques * Familiarize the;
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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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Unlock modern machine learning and deep learning techniques with Python by using the latest cutting-edge open source Python libraries;
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Teaches the machine learning process for business students and professionals using automated machine learning, a new development in data;
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Teaches the machine learning process for business students and professionals using automated machine learning, a new development in data;
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Solve your AI and machine learning problems using complete and real-world code examples. Using a problem-solution approach, this book makes;
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mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their;
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mining and machine learning. Data mining, machine learning and nature-inspired computation are current hot research topics due to their;
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Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;
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required to put machine learning to work in their own applications, using the Core ML APIs What you will learn Understand components of an ML;
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and evaluation Understand Oracle offerings for machine learning Develop machine learning with Oracle database using the built-in machine;
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Colloquial Gujarati your best choice in personal language learning? * interactive - lots of dialogues and exercises for regular practice * clear;
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Colloquial Gujarati your best choice in personal language learning? * interactive - lots of dialogues and exercises for regular practice * clear;
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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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practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow;
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for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code;
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for deployment on mobile devices, using machine learning classification on method call sequences. Also, it is resilient to common code;
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