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Get up and running with machine learning with F# in a fun and functional way About This Book * Design algorithms in F# to tackle;
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HIGHLIGHT The first and only book on Apache Mahout, an open source tool for leveraging machine learning techniques in large-scale;
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Machine Learning Projects for .NET Developers shows you how to build smarter .NET applications that learn from data, using simple;
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Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for;
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Semi-supervised learning is an important area of machine learning. It deals with problems that involve a lot of unlabeled data and very;
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Gain practical skills in machine learning for finance, healthcare, and retail. This book uses a hands-on approach by providing case studies;
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mechanisms for process and deployment. Seasoned database professionals looking to make the leap into machine learning as a growth path will find much;
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A guide to advances in machine learning for financial professionals, with working Python code Key Features Explore advances in machine;
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XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested;
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learning. It points out the difference between the techniques and their suitability for specific applications. This book also describes different;
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-oriented perspective, modern concepts and methods of DSP including machine learning for audio acoustics and engineering. Content highlights include;
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the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms;
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. Many applications on iPhone use machine learning: Siri to serve voice-based requests, the Photos app for facial recognition, and Facebook to;
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with introduction to F# and prepare yourselves for machine learning using .NET framework. You will be writing a simple linear regression model;
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. Human-in-the-Loop Machine Learning is a practical guide to optimizing the entire machine learning process, including techniques for annotation;
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Python makes machine learning easy for beginners and experienced developers With computing power increasing exponentially and costs;
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DescriptionScikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning;
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and deploy machine learning models to the Amazon Web Services (AWS) cloud Learn all about the AWS services available for machine learning;
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Reinforcement and Systemic Machine Learning for Decision Making explores a newer and growing avenue of machine learning algorithm in the;
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Learning services. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision;
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computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning;
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pattern recognition, autonomous driving, expert systems, and much more. What you'll learn: How to write code for machine learning, adaptive;
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pattern recognition, autonomous driving, expert systems, and much more. What you'll learn:How to write code for machine learning, adaptive;
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heavy. What You Will Learn Write machine learning algorithms in Rust Use Rust libraries for different tasks;
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Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning;
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learning, knowledge acquisition and learning, knowledge intensive learning, machine learning and information retrieval, machine learning for web;
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