实用机器学习

python machine learning 最新版 2nd second edition

python machine learning 最新版 2nd second edition

Python Machine Learning 2nd Edition [Sebastian Raschka]

What you will learn Understand the key frameworks in data science, machine learning, and deep learning Harness the power of the latest Python open source libraries in machine learning Explore machine learning techniques using challenging real-world data Master deep neural network implementation using the TensorFlow library Learn the mechanics of classification algorithms to implement the best tool for the job Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Delve deeper into textual and social media data using sentiment analysis

Python Machine Learning 2nd Edition

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition

Python Machine Learning 第二版

Python Machine Learning 第二版 Sebastian Raschka著 高清带书签

Python－Machine－Learning－2nd－Edition

Python Machine Learning第二版，原版PDF。About This Book Leverage Python' s most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets

Python Machine Learning(2nd)

Python Machine Learning(2nd)Python Machine Learning(2nd)Python Machine Learning(2nd)

An Introduction to Machine Learning, 2nd Edition

This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work. Table of Contents Chapter 1 A Simple Machine-Learning Task Chapter 2 Probabilities: Bayesian Classifiers Chapter 3 Similarities: Nearest-Neighbor Classifiers Chapter 4 Inter-Class Boundaries: Linear And Polynomial Classifiers Chapter 5 Artificial Neural Networks Chapter 6 Decision Trees Chapter 7 Computational Learning Theory Chapter 8 A Few Instructive Applications Chapter 9 Induction Of Voting Assemblies Chapter 10 Some Practical Aspects To Know About Chapter 11 Performance Evaluation Chapter 12 Statistical Significance Chapter 13 Induction In Multi-Label Domains Chapter 14 Unsupervised Learning Chapter 15 Classifiers In The Form Of Rulesets Chapter 16 The Genetic Algorithm Chapter 17 Reinforcement Learning

Python Machine Learning 2nd Edition by Sebastian Raschka,‎ Vahid Mirjalili

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition epup格式，但可以用PDF阅读器直接打开。

PythonMachineLearningSecondEdition_Code.rar

Python Machine Learning 2nd Edition [Sebastian Raschka] 代码

Python Machine Learning - 2E BySebastian Raschka

Python Machine Learning - 2E BySebastian Raschka Python Machine Learning - 2E BySebastian Raschka

Python Machine Learning and Deep Learning with python, sklearn, tf,2nd Edition

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd EditionSep 20, 2017 by Sebastian Raschka and Vahid Mirjalili

Python Machine Learning [by Sebastian Raschka] Mobi

Python Machine Learning: Unlock Deeper Insights into Machine Learning With This Vital Guide to Cutting-edge Predictive Analytics. by Sebastian Raschka. Mobi的格式的，可以放到Kindle上面看。

Python Machine Learning Blueprints 2nd Edition

Who this book is for This book is for machine learning practitioners, data scientists, and deep learning enthusiasts who want to take their machine learning skills to the next level by building real-world projects. The intermediate-level guide will help you to implement libraries from the Python ecosystem to build a variety of projects addressing various machine learning domains. Knowledge of Python programming and machine learning concepts will be helpful. Table of Contents: The Python Machine Learning Ecosystem Build an App to Find Underpriced Apartments Build an App to Find Cheap Airfares Forecast the IPO Market Using Logistic Regression Create a Custom Newsfeed Predict whether Your Content Will Go Viral Use Machine Learning to Forecast the Stock Market Classifying Images with Convolutional Neural Networks Building a Chatbot Build a Recommendation Engine What's next?

Python Machine Learning (2nd) -2017-9

Table of Contents Giving Computers the Ability to Learn from Data Training Simple Machine Learning Algorithms for Classification A Tour of Machine Learning Classifiers Using Scikit-Learn Building Good Training Sets - Data Preprocessing Compressing Data via Dimensionality Reduction Learning Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying Machine Learning to Sentiment Analysis Embedding a Machine Learning Model into a Web Application Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data - Clustering Analysis Implementing a Multilayer Artificial Neural Network from Scratch Parallelizing Neural Network Training with TensorFlow Going Deeper - The Mechanics of TensorFlow Classifying Images with Deep Convolutional Neural Networks Modeling Sequential Data using Recurrent Neural Networks

Machine Learning with Spark - 2nd Edition

Throughout this book, we will focus on real-world applications of machine learning technology. While we may briefly delve into some theoretical aspects of machine learning algorithms and required maths for machine learning, the book will generally take a practical, applied approach with a focus on using examples and code to illustrate how to effectively use the features of Spark and MLlib, as well as other well-known and freely available packages for machine learning and data analysis, to create a useful machine learning system.

Machine Learning with Spark 2nd Edition

Machine Learning with Spark 2nd Edition B01DPR2ELW 本书结合案例研究讲解Spark 在机器学习中的应用，并介绍如何从各种公开渠道获取用于机器学习系统的数据。内容涵盖推荐系统、回归、聚类、降维等经典机器学习算法及其实际应用。第2版新增了有关机器学习数学基础以及Spark ML Pipeline API 的章节，内容更加系统、全面、与时俱进。

2017.Python Machine Learning, Second Edition

2017. Python Machine Learning, Second Edition. Machine Learning and Deep Learning with Python, scikit-learn and TensorFlow. Sebastian Raschka.Vahid Mirjalili Packt

python-machine-learning-book-3rd-edition：“머신교과서”（，2020）3판의

Se Se Se（Sebastian Raschka）미자리리리（Vahid Mirjalili）셀러베트스베셀러“ ” 。 주세요이나오류가있다블면이그블로그블로 알려주세요주세요주세요주세요 주세요주세요주세요 교과서1저장소는다음과다（1...

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php-sebastian-recursion-context3-3.0.0-1.el7.remi.noarch.rpm

php-sebastian-exporter3-3.1.2-1.el7.remi.noarch.rpm

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Python for Bioinformatics, Second Edition-Sebastian Bassi

Python for Bioinformatics, Second Edition_Sebastian Bassi

php-sebastian-diff3-3.0.2-1.el7.remi.noarch.rpm

php-sebastian-diff3-3.0.2-1.el7.remi.noarch.rpm

[Raschka]_Python_Machine_Learning(Book4You).pdf

This second edition of Sebastian Raschka’s bestselling book, Python Machine Learning, is now thoroughly updated using the latest Python open source libraries, so that you can understand and work at ...

php-sebastian-comparator3-3.0.2-1.el7.remi.noarch.rpm

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ChatGPT教程（终极版）最全整理

这是一篇动了某些人利益的良心教程。 这是一篇姗姗来迟的ChatGPT教程。 纯小白关于ChatGPT入门，你看我这篇文章就够了。 如果你已经用上了ChatGPT，更要恭喜你挖到宝藏，后面的高级技巧一定能让你有收获。 文章包含以下内容： 一、ChatGPT是啥？有什么用； 二、ChatGPT如何注册； 三、ChatGPT使用方法； 四、用ChatGPT搞钱； 五、高级技巧；

博客中Kmeans以及FCM算法数据（免积分）

博客中Kmeans以及FCM算法的数据，包括IRIS鸢尾花数据集、Wine葡萄酒数据集、Seed小麦种子数据集、glass数据集、WDBD乳腺癌数据集，下载在直接存入项目文件夹即可，如果下载不了，可以私信我，看到后会及时回复。

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用于无法访问hugging face并需要运行stable-diffusion-webui时使用