利用.NET框架打造智能应用：实战机器学习教程

Mastering
.NET
Machine
Learning
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《精通.NET机器学习》(PACKT,2016)是一本专为.NET开发者设计的实战指南，介绍了如何在该平台上集成和利用机器学习技术提升应用程序的智能化水平。本书涵盖了从基础到高级的主题，旨在帮助读者掌握最新的.NET框架（包括.NET Core 1.0），特别是如何在实际业务场景中应用机器学习。 首先，章节1引导读者了解机器学习的基本概念，以及为什么选择.NET作为开发平台。作者强调了自定义机器学习模型的重要性，提倡使用开放数据进行实验，并特别推荐了使用F#语言，因为其简洁且适合处理数学运算和函数式编程。为了准备学习，书中指导如何设置Visual Studio开发环境，并简要介绍了Math.NET、Accord.NET和numl这三个流行的.NET机器学习库。 第二部分深入到实际操作，通过AdventureWorks Regression示例，读者将学习如何构建简单的线性回归模型。从准备测试数据，计算标准差和皮尔逊相关系数，到利用Math.NET和Accord.NET实现回归，书中的实例演示了如何在.NET框架下执行这些统计分析。作者还探讨了回归模型的实际应用，比如与真实数据对比，以验证模型的准确性。 接下来，书籍进一步探讨数据预处理和特征工程，介绍k-means聚类和主成分分析（PCA）的实现，分别使用Accord.NET和numl。读者将学会如何运用神经网络，如AzureML和Accord.NET，来构建更复杂的科学应用，以及如何处理大规模数据集，如MBrace技术。 本书还涉及决策树和贝叶斯分类器，让读者了解如何发现隐藏的模式并优化业务流程。k-近邻算法(k-NN)和朴素贝叶斯分类也被详细讲解，以揭示更多的潜在规律。最后，章节还展示了如何将机器学习模型部署到物联网设备上，实现实时学习和适应。 《精通.NET机器学习》适合对机器学习感兴趣并希望将其应用于.NET项目的开发者，无论他们是初学者还是有一定经验的开发者，都能从中学到如何利用最新的.NET工具和技术提升应用的智能水平。通过阅读本书，读者将具备编写自己的机器学习应用程序的能力，以及使用ASP.NET等最新技术进行商业决策支持的能力。

Mastering Machine Learning Algorithms 2018

2018-06-19 上传
Explore and master the most important algorithms for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how to train, optimize, and validate them Work with Autoencoders and Generative Adversarial Networks Apply label spreading and propagation to large datasets Explore the most important Reinforcement Learning techniques Who This Book Is For This book is an ideal and relevant source of content for data science professionals who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. A basic knowledge of machine learning is preferred to get the best out of this guide. Table of Contents Machine Learning Model Fundamentals Introduction to Semi-Supervised Learning Graph-based Semi-Supervised Learning Bayesian Networks and Hidden Markov Models EM algorithm and applications Hebbian Learning Advanced Clustering and Feature Extraction Ensemble Learning Neural Networks for Machine Learning Advanced Neural Models Auto-Encoders Generative Adversarial Networks Deep Belief Networks Introduction to Reinforcement Learning Policy estimation algorithms

Mastering Machine Learning with scikit-learn [2017,第二版]

2017-08-16 上传
Mastering Machine Learning with scikit-learn - Second Edition by Gavin Hackeling English | 24 July 2017 | ASIN: B06ZYRPFMZ | ISBN: 1783988363 | 254 Pages | AZW3 | 5.17 MB Key Features Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Learn how to build and evaluate performance of efficient models using scikit-learn Practical guide to master your basics and learn from real life applications of machine learning Book Description Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model. This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learn's API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your model's performance. By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach. What you will learn Review fundamental concepts such as bias and variance Extract features from categorical variables, text, and images Predict the values of continuous variables using linear regression and K Nearest Neighbors Classify documents and images using logistic regression and support vector machines Create ensembles of estimators using bagging and boosting techniques Discover hidden structures in data using K-Means clustering Evaluate the performance of machine learning systems in common tasks About the Author Gavin Hackeling is a data scientist and author. He was worked on a variety of machine learning problems, including automatic speech recognition, document classification, object recognition, and semantic segmentation. An alumnus of the University of North Carolina and New York University, he lives in Brooklyn with his wife and cat. Table of Contents The Fundamentals of Machine Learning Simple linear regression Classification and Regression with K Nearest Neighbors Feature Extraction and Preprocessing From Simple Regression to Multiple Regression From Linear Regression to Logistic Regression Naive Bayes Nonlinear Classification and Regression with Decision Trees From Decision Trees to Random Forests, and other Ensemble Methods The Perceptron From the Perceptron to Support Vector Machines From the Perceptron to Artificial Neural Networks Clustering with K-Means Dimensionality Reduction with Principal Component Analysis

C# Machine Learning Projects pdf

2018-06-22 上传
Power your C# and .NET applications with exciting machine learning models and modular projects Key Features Produce classification, regression, association, and clustering models Expand your understanding of machine learning and C# Get to grips with C# packages such as Accord.net, LiveCharts, and Deedle Book Description Machine learning is applied in almost all kinds of real-world surroundings and industries, right from medicine to advertising; from finance to scientifc research. This book will help you learn how to choose a model for your problem, how to evaluate the performance of your models, and how you can use C# to build machine learning models for your future projects. You will get an overview of the machine learning systems and how you, as a C# and .NET developer, can apply your existing knowledge to the wide gamut of intelligent applications, all through a project-based approach. You will start by setting up your C# environment for machine learning with the required packages, Accord.NET, LiveCharts, and Deedle. We will then take you right from building classifcation models for spam email fltering and applying NLP techniques to Twitter sentiment analysis, to time-series and regression analysis for forecasting foreign exchange rates and house prices, as well as drawing insights on customer segments in e-commerce. You will then build a recommendation model for music genre recommendation and an image recognition model for handwritten digits. Lastly, you will learn how to detect anomalies in network and credit card transaction data for cyber attack and credit card fraud detections. By the end of this book, you will be putting your skills in practice and implementing your machine learning knowledge in real projects. What you will learn Set up the C# environment for machine learning with required packages Build classification models for spam email filtering Get to grips with feature engineering using NLP techniques for Twitter sentiment analysis Forecast foreign exchange rates using continuous and time-series data Make a recommendation model for music genre recommendation Familiarize yourself with munging image data and Neural Network models for handwritten-digit recognition Use Principal Component Analysis (PCA) for cyber attack detection One-Class Support Vector Machine for credit card fraud detection Who this book is for If you're a C# or .NET developer with good knowledge of C#, then this book is perfect for you to get Machine Learning into your projects and make smarter applications. Table of Contents Basics of machine learning modeling Spam email filtering Twitter sentiment analysis Foreign exchange rate forecast Fair value of house/property Customer segmentation Music genre recommendation Handwritten digit recognition Cyber attack detection Credit card fraud detection What is next?

Mastering-Machine-Learning-Algorithms:精通机器学习算法，由Packt发布

2021-05-28 上传
掌握机器学习算法 这是出版的的代码库。 它包含从头到尾完成本书所必需的所有支持项目文件。 关于这本书 机器学习是AI的子集，旨在使现代计算机系统变得越来越智能。 机器学习的真正力量在于它的算法，该算法甚至使...

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2021-05-27 上传
这是Packt发布的的代码存储库。 使用SageMaker，Apache Spark和TensorFlow在Python中进行高级机器学习 这本书是关于什么的？ AWS一直在推动创新，使数据科学家能够探索各种机器学习（ML）云服务。 本书是您在AWS...

Mastering Machine Learning with R, 2nd Edition-Packt Publishing(2017).epub

2018-03-15 上传
So, I've tried to provide enough of the skills and tools, but not all of them, to get a reader up and running with R and machine learning as quickly and painlessly as possible. I think I've added ...

Mastering Machine Learning with Spark 2.x-Packt Publishing(2017).pdf

2018-03-11 上传
Big data – that was our motivation to explore the world of machine learning with Spark a couple of years ago. We wanted to build machine learning applications that would leverag models trained on ...

层次分析matlab代码-Mastering-Machine-Learning-with-MATLAB:Packt出版的《使用MATLAB掌握

2021-05-23 上传
层次分析matlab代码使用MATLAB掌握机器学习[视频] 这是的代码存储库，由发行。 它包含从头到尾完成视频课程所需的所有支持项目文件。 关于视频课程 对于许多机器学习的研究人员和数学专家来说，MATLAB是首选的语言。...

Mastering Apache Spark(PACKT,2015)

2015-10-29 上传
Apache Spark is an in-memory cluster based parallel processing system that provides a wide range of functionality like graph processing, machine learning, stream processing and SQL. It operates at ...

Mastering-Machine-Learning-Algorithms-Second-Edition:精通机器学习算法第二版，由Packt发行

2021-05-26 上传
精通机器学习算法-第2版 这是出版的的代码存储库。 它包含从头到尾完成本书所必需的所有支持项目文件。 关于这本书 新的第二版畅销书籍指南，用于探索和掌握用于解决复杂机器学习问题的最重要算法，其更新范围包括...
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