Python数据挖掘与机器学习实战
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【2018新书】机器学习与数据科学前沿(Advances in Machine Learning and Data Science) 评分:
【2018新书】机器学习与数据科学前沿(Advances in Machine Learning and Data Science)
上传时间:2018-06 大小:14.2MB
7.20MB
Hands-On Data Science and Python Machine Learning
2017-08-15Hands-On Data Science and Python Machine Learning by Frank Kane English | 31 July 2017 | ISBN: 1787280748 | ASIN: B072QBVXGH | 420 Pages | AZW3 | 7.21 MB Key Features Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Book Description Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. What you will learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform
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Deep Learning in Python: Master Data Science and Machine Learning with Modern
2018-07-30Deep learning is making waves. At the time of this writing (March 2016), Google’s AlghaGo program just beat 9-dan professional Go player Lee Sedol at the game of Go, a Chinese board game. Experts in the field of Artificial Intelligence thought we were 10 years away from achieving a victory against a top professional Go player, but progress seems to have accelerated! While deep learning is a complex subject, it is not any more difficult to learn than any other machine learning algorithm. I wrote this book to introduce you to the basics of neural networks. You will get along fine with undergraduate-level math and programming skill. All the materials in this book can be downloaded and installed for free. We will use the Python programming language, along with the numerical computing library Numpy. I will also show you in the later chapters how to build a deep network using Theano and TensorFlow, which are libraries built specifically for deep learning and can accelerate computation by taking advantage of the GPU. Unlike other machine learning algorithms, deep learning is particularly powerful because it automatically learns features. That means you don’t need to spend your time trying to come up with and test “kernels” or “interaction effects” - something only statisticians love to do. Instead, we will let the neural network learn these things for us. Each layer of the neural network learns a different abstraction than the previous layers. For example, in image classification, the first layer might learn different strokes, and in the next layer put the strokes together to learn shapes, and in the next layer put the shapes together to form facial features, and in the next layer have a high level representation of faces. On top of all this, deep learning is known for winning its fair share Kaggle contests. These are machine learning contests that are open to anyone in the world who are allowed to use any machine learning technique they want. Deep learning is that powerful. Do you want a gentle introduction to this “dark art”, with practical code examples that you can try right away and apply to your own data? Then this book is for you. Who is this book NOT for? Deep Learning and Neural Networks are usually taught at the upper-year undergraduate level. That should give you some idea of the type of knowledge you need to understand this kind of material. You absolutely need exposure to calculus to understand deep learning, no matter how simple the instructor makes things. Linear algebra would help. I will assume familiarity with Python (although it is an easy language to pick up). You will need to have some concept of machine learning. If you know about algorithms like logistic regression already, this book is perfect for you. If not, you might want to check out my “prerequisites” book, at: http://amzn.com/B01D7GDRQ2 On the other hand, this book is more like a casual primer than a dry textbook. If you are looking for material on more advanced topics, like LSTMs, convolutional neural networks, or reinforcement learning, I have online courses that teach this material, for example: https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow New libraries like TensorFlow are being updated constantly. This is not an encyclopedia for these libraries (as such a thing would be impossible to keep up to date). In the one (1!!!) month since the book was first published, no less than THREE new wrapper libraries for TensorFlow have been released to make coding deep networks easier. To try and incorporate every little update would not only be impossible, but would continually cause parts of the book to be obsolete. Nobody wants that. This book, rather, includes fundamentals. Understanding these building blocks will make tackling these new libraries and features a piece of cake - that is my goal.
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Advances in Machine Learning and Data Science Recent Achievements and 无水印原版pdf
2018-05-23Advances in Machine Learning and Data Science Recent Achievements and Research Directives 英文无水印原版pdf pdf所有页面使用FoxitReader、PDF-XChangeViewer、SumatraPDF和Firefox测试都可以打开 本资源...
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Advances in Machine Learning1
2018-03-23Advances in Machine Learning1 Advances in Machine Learning1
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Advances in Financial Machine Learning
2018-07-31Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the ...
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Advances in Machine Learning 机器学习进展
2022-11-27Advances in Machine Learning 机器学习进展
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Advances and Open Problems in Federated Learning.pdf
2020-05-13from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection ...
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Advances_in_Financial_Machine_Learning_001_resultccn_量化_量化交易_金融_
2021-09-30量化交易金融中的机器学习,真心不错 Advances in Financial Machine Learning
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Recent Advances in Big Data and Deep Learning
2019-04-26Recent Advances in Big Data and Deep Learning This book presents 39 original articles that have been accepted to the 2019 INNS Big Data and Deep Learning (INNS BDDL), a major event for researchers ...
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Advances in Machine Learning and Signal Processing
2018-02-06Editors (view affiliations) Ping Jack SohWai Lok WooHamzah Asyrani SulaimanMohd Azlishah OthmanMohd Shakir Saat
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Practical Machine Learning with Python.pdf
2018-07-25in Machine Learning solutions and driving adoption across diverse segments of the industry. The ability to learn complex models underlying the real-world processes from observed (training) data ...
52.36MB
Advances in Computer Science and Ubiquitous Computing
2016-04-19Advances in Computer Science and Ubiquitous Computing
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Advances in Knowledge Discovery and Data Mining
2018-11-20这是关于知识发现与数据挖掘的论文集,高清,最新版本,经典著作,英文版
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Recent Advances in Deep Learning: An Overview
2018-08-16Recent Advances in Deep Learning: An Overview(深度学习最新进展综述),这篇综述论文列举出了近年来深度学习的重要研究成果,从方法、架构,以及正则化、优化技术方面进行概述。
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博客中聚类算法(K-means、FCM、DBSCAN、DPC)的数据集(免积分)
2023-05-16博客中K-means、FCM、DBSCAN、DPC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,以及几个人工数据集常用的人工数据集(Flame、Spiral等),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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机器学习期末复习题及答案
2023-03-19机器学习期末复习题及答案
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中文短信数据集-带标签
2024-02-05本数据集可用于进行文本分类、信息检索等自然语言处理实验,共包含80万条短信。其中:原始数据集data.txt每行为1条短信,格式为“标签\t短信内容”,标签=0表示正常短信,标签=1表示垃圾短信。train.csv和test.csv为拆分后的训练集与测试集,拆分代码为train_test_split.py。stopwords.txt为使用的停用词。 基于该数据集的文本分类详见文章https://blog.csdn.net/baidu_40395808/article/details/135793836,基于该数据集的信息检索详见文章https://blog.csdn.net/baidu_40395808/article/details/135897480。 示例如下: 0 商业秘密的秘密性那是维系其商业价值和垄断地位的前提条件之一 1 《依林美容》三.八.女人节倾情大放送活动开始啦!!!!超值套餐等你拿,活动时间x月x日一x月xx日, 详情进店咨询。美丽热线x
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神经网络回归预测--气温数据集
2021-11-26神经网络回归预测--气温数据集
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Mathwork+Matlab+编程手册
2023-08-25Introduction to Programming with MATLAB ~ Vanderbilt University
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XGBoost+LightGBM+LSTM-光伏发电量预测
2022-12-24包含比赛代码、数据、训练后的神经网络模型等。 在分析光伏发电原理的基础上,论证了辐照度、光伏板工作温度等影响光伏输出功率的因素,通过实时监测的光伏板运行状态参数和气象参数建立预测模型,预估光伏电站瞬时发电量,根据光伏电站DCS系统提供的实际发电量数据进行对比分析,验证模型的实际应用价值。 1 数据探索与数据预处理 1.1 赛题回顾 1.2 数据探索性分析与异常值处理 1.3 相关性分析 2 特征工程 2.1 光伏发电领域特征 2.2 高阶环境特征 3 模型构建与调试 3.1 预测模型整体结构 3.2 基于LightGBM与XGBoost的构建与调试 3.3 基于LSTM的模型构建与调试 3.4 模型融合与总结 4 总结与展望 参考文献
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时间序列预测模型实战案例(Xgboost)(Python)(机器学习)包括时间序列预测和时间序列分类,点击即可运行!
2023-09-25内容概要 资源包括三部分(时间序列预测部分和时间序列分类部分和所需的测试数据集全部包含在内) 在本次实战案例中,我们将使用Xgboost算法进行时间序列预测。Xgboost是一种强大的梯度提升树算法,适用于各种机器学习任务,它最初主要用于解决分类问题,在此基础上也可以应用于时间序列预测。 时间序列预测是通过分析过去的数据模式来预测未来的数值趋势。它在许多领域中都有广泛的应用,包括金融、天气预报、股票市场等。我们将使用Python编程语言来实现这个案例。 其中包括模型训练部分和保存部分,可以将模型保存到本地,一旦我们完成了模型的训练,我们可以使用它来进行预测。我们将选择合适的输入特征,并根据模型的预测结果来生成未来的数值序列。最后,我们会将预测结果与实际观测值进行对比,评估模型的准确性和性能。 适合人群:时间序列预测的学习者,机器学习的学习者, 能学到什么:本模型能够让你对机器学习和时间序列预测有一个清楚的了解,其中还包括数据分析部分和特征工程的代码操作 阅读建议:大家可以仔细阅读代码部分,其中包括每一步的注释帮助读者进行理解,其中涉及到的知识有数据分析部分和特征工程的代码操作。
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亚博K210模型训练部署
2023-07-11亚博K210模型训练部署
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hugging face的models-openai-clip-vit-large-patch14文件夹
2023-10-25用于无法访问hugging face并需要运行stable-diffusion-webui时使用
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Plecs电力电子仿真PLECS41.64 电力系统仿真软件免安装版本
2023-02-02超级容易学习的电力电子仿真软件。 PLECS是一个用于电路和控制结合的多功能仿真软件,尤其适用于电力电子和传动系统。 对于仿真Buck电路,Boost电路,单相整流电路,单相逆变电路,三相整流电路,三相逆变电路,LLC电路等都能够完美实现。并且能够直接使用C语言进行编程仿真,让代码直接写完以后完整运行到控制板中。 Plecs主要解决了Matlab Simulink仿真速度很慢,学习过程复杂和困难等问题。 Plexim在电力电子仿真领域处于世界领先地位。首要软件产品PLECS可以协助用户加快产品开发和创新流程。其优势来源于先进的编程技术,以及融合了非常规建模概念的仿真算法。通过认真听取工程师的需求和建议,我们能够为客户的今天和未来提供满意的解决方案。 从2002年开始,PLECS已经在众多工业领域成为电力电子仿真的标准。典型应用涵盖新能源,车辆电子,航空航天,工业及牵引驱动等。
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Stable-Diffusion WEBUI 简体中文语言包(2023.05.30更新)
2023-05-30AI绘图,Stable-Diffusion WEBUI,本地化(简体中文)语言文件。 原始文件来自翻译插件,根据自己实际使用情况,增加和修改了一些翻译。 配合【双语插件】看上去要自然一点,内容还在继续完善中。 本次增加了一些翻译内容,特别是插件。 同时继续合并了其它翻译插件的内容。 最近文字提示修改得有点多啊。 请放入“你的SDWebUI项目位置/localizations/”中。 中文翻译部分删掉了不少括起来的英文原文,所以别直接选它用。 请配合【Bilingual Localization】插件使用,双语同时显示,效果最好。
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Matlab 基于支持向量机(SVM)的数据回归预测 SVM回归
2023-06-131. Matlab实现支持向量机的数据回归预测(完整源码和数据) 2. 多变量输入,单变量输出,数据回归预测 3. 评价指标包括:R2、MAE、MSE、RMSE 4. 包括拟合效果图和散点图 5. Excel数据,暂无版本限制,推荐2018B及以上版本 注:采用 Libsvm 工具箱(无需安装,可直接运行),仅支持 Windows 64位系统