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【英美最新书籍】《Python Machine Learning》(第2版)【已搜索，无重复】
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【英美最新书籍】《Python Machine Learning》(第2版)【已搜索，无重复】 作者： Sebastian Raschka (Author), Vahid Mirjalili (Author) 出版社：Packt Publishing 出版时间：2017 (第2版) Language: English 页码：622 页 文件格式：PDF 文件大小：10.7 MB 书籍简介： . Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world. If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn. 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 书籍目录： Table of Contents 1. Giving Computers the Ability to Learn from Data 2. Training Simple Machine Learning Algorithms for Classification 3. A Tour of Machine Learning Classifiers Using Scikit-Learn 4. Building Good Training Sets - Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data - Clustering Analysis 12. Implementing a Multilayer Artificial Neural Network from Scratch 13. Parallelizing Neural Network Training with TensorFlow 14. Going Deeper - The Mechanics of TensorFlow 15. Classifying Images with Deep Convolutional Neural Networks 16. Modeling Sequential Data using Recurrent Neural Networks 书籍封面： .  |
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2018-03-18 03:54:24, 10.79 M
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