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Python Machine Learning (2nd) -2017-9

2017-09-22 上传大小:15.93MB
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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 Targ
                            et 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

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, Who This Book Is For, If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource., What You Will Learn, Explore how to use different machine learning models to ask different questions of your data, Learn how to build neural networks using Keras and Theano, Find out how to write clean and elegant Python code that will optimize the strength of your algorithms, Discover how to embed your machine learning model in a web application for increased accessibility, Predict continuous target outcomes using regression analysis, Uncover hidden patterns and structures in data with clustering, Organize data using effective pre-processing techniques, Get to grips with sentiment analysis to delve deeper into textual and social media data, Style and approach, Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.
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评论共有2条

hugh37 2017-12-29 10:34:02
书不错,积分要的太多了
technicaec7 2017-12-16 13:48:08
结构清晰 作者用实例一步一步讲解深度学习中的各个概念 适合入门
 

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