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Machine Learning Control by Symbolic Regression

specialists in the field of control, Machine Learning Control by Symbolic Regression opens up a new promising direction of research and acquaints;

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Machine Learning with Scala Quick Start Guide

covering deep learning and machine learning basics. It then explains how to use Scala-based ML libraries to solve classification and regression;

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Machine Learning with scikit-learn Quick Start Guide

Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering;

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Learning from Data for Aquatic and Geotechnical Environments

), classification, regression and control as the learning tasks. A unified methodology based on the concepts of machine learning, information theory and;

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Learning from Data for Aquatic and Geotechnical Environments

), classification, regression and control as the learning tasks. A unified methodology based on the concepts of machine learning, information theory and;

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Introduction to Machine Learning with R

frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression;

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Symbolic Visual Learning

utilize previous adaptations in changing environments. Symbolic Visual Learning presents research which adds visual learning capabilities to;

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Artificial Intelligence, Machine Learning, and Deep Learning

This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will;

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Mastering Machine Learning with scikit-learn -

and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book;

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Machine Learning Algorithms

foundation for your entry into Machine Learning by strengthening your roots (algorithms) with this comprehensive guide. Who This Book Is For;

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Go Machine Learning Projects

learning ecosystem Implement clustering, regression, classification, and neural networks with Go Get to grips with libraries such as Gorgonia;

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Test-Driven Machine Learning

Control your machine learning algorithms using test-driven development to achieve quantifiable milestones About This Book * Build;

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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction

generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and;

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Machine Learning Algorithms

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms Key Features;

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Linear Algebra and Optimization for Machine Learning

accuracy of regression and classification models. The parent problem of optimization-centric machine learning is least-squares regression;

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Linear Algebra and Optimization for Machine Learning

accuracy of regression and classification models. The parent problem of optimization-centric machine learning is least-squares regression;

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Machine Learning and Deep Learning Using Python and TensorFlow

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to;

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Machine Learning Pocket Reference Working with Structured Data in Python

With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author;

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Machine Learning for Developers

by leveraging the power of Machine Learning * A highly practical guide explaining the concepts of problem solving in the easiest possible;

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Computational Learning Theory & Natural Learning Systems V 2 - Intersections Between Theory & Experiment

areas of learning research, computational learning theory, neural network research and symbolic machine learning.;

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Deep Learning Applications

ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression;

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Machine Learning with PySpark

supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You'll also see unsupervised;

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Machine Learning for OpenCV

learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all;

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Mastering Machine Learning with R

learning methods * Implement the numerous powerful features offered by R with this comprehensive guide to building an independent R-based ML;

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Training Systems Using Python Statistical Modeling

Python's rich suite of libraries for statistical modeling Implement regression, clustering and train neural networks from scratch Includes real;

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Machine Learning Foundations

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning;

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Einde inhoud

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