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Sculpting Data for ML

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Practical Java Machine Learning

Build machine learning (ML) solutions for Java development. This book shows you that when designing ML apps, data is the key driver and;

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Machine Learning

explainability amount to specific design choices for the model, data, and loss of a ML method.;

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Mastering Azure Machine Learning

choose the right Azure service for different ML tasks. It then focuses on Azure Machine Learning and takes you through the process of data;

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Machine Learning with R, tidyverse, and mlr

readers to the tidyverse, a powerful set of R tools designed specifically for practical data science. Key Features * Commonly used;

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Machine Learning with Core ML

in the context of mobile platforms (specifically iOS). You'll learn to implement Core ML for visual-based applications using the principles;

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Machine Learning Design Patterns

. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different;

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Data Science with SQL Server Quick Start Guide

Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data;

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Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement;

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Human and Machine Learning

With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement;

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Adversary-Aware Learning Techniques and Trends in Cybersecurity

) and cyber security up-to-date and in-depth knowledge of recent techniques for improving the vulnerabilities of AIML systems against attacks;

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Multimodal Learning for Clinical Decision Support and Clinical Image Based Proce

multimodal data sets for clinical decision support and treatment planning. The CLIP workshops provides a forum for work centered on specific clinical;

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Machine Learning for iOS Developers

, 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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Python Machine Learning for Beginners

Pandas Library for Data AnalysisData Visualization via Matplotlib, Seaborn, and Pandas LibrariesSolving Regression Problems in ML Using Sklearn;

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Machine Learning with Amazon SageMaker Cookbook

create high-quality models Work with effective data analysis and preparation techniques Explore solutions for debugging and managing ML;

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Machine Learning for Computer Scientists and Data Analysts

helps readers to understand the concepts of ML algorithms and enables them to develop the skills necessary to choose an apt ML algorithm for a;

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Supervised Machine Learning

of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness;

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Supervised Machine Learning

of statistical experiments to identify optimal feature subsets and optimal hyper-parameters for ML methods, data contamination to test for the robustness;

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Artificial Intelligence For High Energy Physics

applied modern machine learning (ML) techniques to all stages of the data analysis pipeline, from raw data processing to statistical analysis. The;

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Machine Learning in Cognitive IoT

). Machine learning is explored by covering all the technical issues and various models used for data analytics during decision making at different;

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Purely Functional Data Structures

Most books on data structures assume an imperative language like C or C++. However, data structures for these languages do not always;

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Purely Functional Data Structures

Most books on data structures assume an imperative language like C or C++. However, data structures for these languages do not always;

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Introduction To Machine Learning In Quantitative Finance, An

uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsFeatured with the;

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Introduction To Machine Learning In Quantitative Finance, An

uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsFeatured with the;

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Machine Learning in Asset Pricing

environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML;

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Machine Learning for Adaptive Many Core Machines A Practical Approach

handle Big Data. This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit;

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Machine Learning with Microsoft Technologies

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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