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with TensorFlow and other libraries Book DescriptionMachine learning (ML) is widely used in many industries such as science, healthcare, and;
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well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you;
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well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you;
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execute style transfer on images Who this book is forMachine Learning with Core ML is for you if you are an intermediate iOS developer interested;
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Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively Key Features Build;
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, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple's ML services, Machine Learning for iOS Developers;
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learning models efficiently on Spark using Azure Databricks Deploy machine learning models for batch and real-time scoring with Azure Kubernetes;
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want to implement various machine-learning concepts in the context of Spark ML, this book is for you. You should be well versed with the Scala and;
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supervised and unsupervised ML algorithms through real-world examples. This book will get you hands-on with tuning and optimizing a model for;
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Learning includes multiple projects, with particular focus on the Android mobile platform and features such as sensors, camera, and connectivity;
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you with a hands-on approach to learn ML fast.How Is This Book Different?AI Publishing strongly believes in learning by doing methodology;
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With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement;
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With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement;
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ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML;
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best ML model for your task * A variety of hands-on coding exercises * ML best practices For readers with basic;
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Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on;
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about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services;
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, time series forecasting, and computer vision models to solve ML problems. You'll explore various solutions for working with deep learning;
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machine learning (ML), in healthcare and within cardiovascular medicine. The book focuses on emphasizing ML for biomedical applications and;
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Get up and running, and productive in no time with MLflow using the most effective machine learning engineering approach Key Features;
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high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to;
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Browser, you'll be on your way to becoming an experienced Machine Learning developer. What You'll Learn Work with ML models;
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in Medical Image Computing, iMIMIC 2019, and the 9th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2019, held;
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learning concepts and applications. You'll learn not only theory, but also dive into code samples and example projects with TensorFlow.js;
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manage your image and text data with Machine Learning A A A Reuse examples as-is to deploy your models A A A Understand the ML model;
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is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages;
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. Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R;
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Einde inhoud
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