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machine learning algorithms from scratch (pdf + 源码) 评分:
本书作者是网路有名机器学习专家,本书全以python撰写,并且避免使用第三方函式库,完全让读者了解各种机学习演算的实作,在讲求快速的年代,能真正彻底了解演算法的实作,而不是用keras、tensor flow,组一组、拼一拼,实做了一个四不像的ML演算法,让读者从理论到实作(完全实作),是本不可多得的好书。
上传时间:2017-08 大小:1.89MB
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[machine_learning_mastery系列]Master_Machine_Learning_Algorithms.pdf
2018-06-05Preface Machine learning algorithms dominate applied machine learning. Because algorithms are such a big part of machine learning you must spend time to get familiar with them and really understand how they work. I wrote this book to help you start this journey. You can describe machine learning algorithms using statistics, probability and linear algebra. The mathematical descriptions are very precise and often unambiguous. But this is not the only way to describe machine learning algorithms. Writing this book, I set out to describe machine learning algorithms for developers (like myself). As developers, we think in repeatable procedures. The best way to describe a machine learning algorithm for us is: 1. In terms of the representation used by the algorithm (the actual numbers stored in a file). 2. In terms of the abstract repeatable procedures used by the algorithm to learn a model from data and later to make predictions with the model. 3. With clear worked examples showing exactly how real numbers plug into the equations and what numbers to expect as output. This book cuts through the mathematical talk around machine learning algorithms and shows you exactly how they work so that you can implement them yourself in a spreadsheet, in code with your favorite programming language or however you like. Once you possess this intimate knowledge, it will always be with you. You can implement the algorithms again and again. More importantly, you can translate the behavior of an algorithm back to the underlying procedure and really know what is going on and how to get the most from it. This book is your tour of machine learning algorithms and I’m excited and honored to be your tour guide. Let’s dive in.
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ml-from-scratch:所有的ML算法和ML模型都是由纯PythonNumpy从头开始编写的,并带有Math的内幕。 它在CPU上运行良好
2021-04-29从头开始学习机器 关于 这个ML储存库是关于Numpy从头开始编写机器学习算法的,并在没有自动区分框架(例如Tensorflow,Pytorch等)的情况下进行了数学运算.Computer Vision,NLP中的某些高级模型需要Tensorflow才能快速将想法写成纸。 储存库结构 作为软件工程师,我遵循OOP的原则来构建存储库。 你可以看到, NeuralNetwork类将使用FCLayer , BatchNormLayer , ActivationLayer类和CNN类将使用ConvLayer , PoolingLayer , FCLayer , ActivationLayer ,......这很容易帮助我重用每一段代码,我写以及为可读的代码。 目录 机器学习模型: 深度学习层: 辍学层 优化算法: 新元 SGD与动量 RMSProp 亚当 权重初始化: 他初始化 Xavier
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deep-learning-from-scratch, 『ゼロから作る Deep Learning』のリポジトリ.zip
2019-09-17deep-learning-from-scratch, 『ゼロから作る Deep Learning』のリポジトリ
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master_machine_learning_algorithms
2018-05-27master_machine_learning_algorithms
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deep_learning_from_scratch_斋藤康毅
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Machine Learning Algorithms(pdf+epub+mobi+code_files).zip
2018-09-09Machine Learning Algorithms(pdf+epub+mobi+code_files).zip
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Machine Learning Algorithms
2018-10-25This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions ...
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Mastering Machine Learning Algorithms 2018
2018-06-19Explore 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 ...
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Machine-Learning-From-Scratch常用机器学习的算法简洁实现
2021-01-17Machine-Learning-From-Scratch常用机器学习的算法简洁实现
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Pro Machine Learning Algorithms Implementing Algorithms in Python
2018-07-01Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R by V Kishore Ayyadevara Bridge the gap between a high-level understanding of how an algorithm works and...
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Machine Learning Algorithms 无水印pdf
2017-10-06Machine Learning Algorithms 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络,如有侵权,请联系上传者或csdn删除 本资源转载自网络,如有侵权,请联系上传者或csdn删除
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Python Machine Learning.pdf 无水印书签修正完美版 2015
2015-11-13原pdf书签没有链接正确,本人对此进行了修正 Paperback: 454 pages Publisher: Packt Publishing - ebooks Account (September 2015) Language: English ISBN-10: 1783555130 ISBN-13: 978-1783555130 Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics 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
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Deep Learning, Vol. 1 From Basics to Practice
2018-07-03Volume 1 – 1 Introduction to Machine Learning 2 Statistics 3 Probability 4 Bayes' Rule 5 Curves And Surfaces 6 Information Theory 7 Classification 8 Training And Testing 9 Overfitting And Underfitting 10 Neurons 11 Learning And Reasoning 12 Data Preparation 13 Classifiers 14 Ensembles 15 Scikit-Learn 16 Feed Forward Networks 17 Activation Functions 18 Backpropagation 19 Optimizers
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ML_Algorithms_From_Scratch:从零开始的ML算法
2021-03-17ML_Algorithms_From_Scratch 从零开始的ML算法
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Machine-Learning-Algorithms-from-Scratch, 从零开始实现机器学习算法.zip
2019-09-17Machine-Learning-Algorithms-from-Scratch, 从零开始实现机器学习算法 Machine-Learning-Algorithms-from-Scratch从零开始实现机器学习算法。目前实现的算法:简单线性回归。数据集:来自Quandl的股票数据逻辑回归...
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Machine Learning Algorithms epub
2017-09-27Machine Learning Algorithms 英文epub 本资源转载自网络,如有侵权,请联系上传者或csdn删除 本资源转载自网络,如有侵权,请联系上传者或csdn删除
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Machine Learning Algorithms pdf format
2018-07-23Machine Learning Algorithms by Giuseppe Bonaccorso English | 24 July 2017 | ISBN: 1785889621 | ASIN: B072QBG11J | 360 Pages | AZW3 | 12.18 MB Build strong foundation for entering the world of Machine...
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Master Machine Learning Algorithms - Discover how they work by Jason Brownlee
2021-06-26Master Machine Learning Algorithms Finally Pull Back The Curtain And See How They Work With Clear Descriptions, Step-By-Step Tutorials and Working Examples in Spreadsheets by Jason Brownlee 10 top ...
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Pro Machine Learning Algorithms Implementing Algorithms in Python epub
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Machine-Learning_from-scratch:这是从头开始的完整机器学习课程!!
2021-03-15机器学习从零开始 只需安装anaconda! 这是从头开始的完整机器学习课程!!
- 18.29MB
Deep Learning with R (MEAP v1)-Manning(2017).pdf
2018-04-01Thank you for purchasing the MEAP for Deep Learning with R. If you are looking for a resource to learn about deep learning from scratch and to quickly become able to use this knowledge to solve real-world problems, you have found the right book. Deep Learning with R is meant for statisticians, analysts, engineers and students with a reasonable amount of R experience, but no significant knowledge of machine learning and deep learning. This book is an adaptation of my previously published Deep Learning with Python, with all of the code examples using the R interface to Keras. The goal of the book is to provide a learning resource for the R community that goes all the way from basic theory to advanced practical applications. Deep learning is an immensely rich subfield of machine learning, with powerful applications ranging from machine perception to natural language processing, all the way up to creative AI. Yet, its core concepts are in fact very simple. Deep learning is often presented as shrouded in a certain mystique, with references to algorithms that “work like the brain”, that “think” or “understand”. Reality is however quite far from this science- fiction dream, and I will do my best in these pages to dispel these illusions. I believe that there are no difficult ideas in deep learning, and that’s why I started this book, based on premise that all of the important concepts and applications in this field could be taught to anyone, with very few prerequisites. This book is structured around a series of practical code examples, demonstrating on real- world problems every the notions that gets introduced. I strongly believe in the value of teaching using concrete examples, anchoring theoretical ideas into actual results and tangible code patterns. These examples all rely on Keras, the deep learning library. When I released the initial version of Keras almost two years ago, little did I know that it would quickly skyrocket to become one of the most widely used deep l
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Python Machine Learning Machine Learning And Deep Learning From Scratch
2020-06-22Python Machine Learning Machine Learning And Deep Learning From Scratch Illustrated With Python, Scikit-Learn, Keras, Theano And Tensorflow by Moubachir Madani Fadoul (z-lib.org).pdf
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ML-From-Scratch:从头开始学习机器。 机器学习模型和算法的裸露NumPy实现重点在于可访问性。 旨在涵盖从线性回归到深度学习的所有内容
2021-02-04从零开始的机器学习 关于 从头开始一些基本机器学习模型和算法的Python实现。 该项目的目的不是要产生尽可能优化和计算高效的算法,而是以透明和可访问的方式展示它们的内部工作原理。 目录 安装 $ git clone https://github.com/eriklindernoren/ML-From-Scratch $ cd ML-From-Scratch $ python setup.py install 例子 多项式回归 $ python mlfromscratch/examples/polynomial_regression.py 图:正则多项式回归模型拟合的训练进度2016年瑞典
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ML-From-Scratch:进行中
2021-04-07ML从头开始 当前存储库包含Python中使用最广泛的机器学习算法的实现。 算法是用python语言编写的。 该实现无需使用任何基于ML的库。 该信息库是一个正在进行的工作,我将在其中实现以下算法: 线性回归: 贝叶斯回归 广义线性模型 逻辑回归 线性判别分析 二次判别分析 朴素贝叶斯 决策树: 回归树 分类树 合奏方法 装袋 随机森林 助推 神经网络 参考和鸣谢
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Machine Learning Algorithms 2017.8
2017-08-17Machine Learning Algorithms By 作者: Giuseppe Bonaccorso ISBN-10 书号: 1785889621 ISBN-13 书号: 9781785889622 Release 出版日期: 2017-08-04 pages 页数: (449) List Price: $49.99 Book Description ...
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机器学习算法Machine Learning Algorithms,
2019-03-30In this book you will learn all the important Machine Learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised ...
- 37.29MB
Practical Machine Learning with Python(pdf书+sourcecode)
2019-04-30This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for ...
- 123KB
from_scratch:从头开始实现的ML示例
2021-02-16从头开始 从头开始实施的ML /优化示例。 该代码库旨在在ML和优化常用功能背后建立直觉。 现在,该代码库包含以下示例: KMeans聚类 主成分分析(PCA):刮擦法使用幂迭代来计算奇异值和特征向量 线性回归:对多个自变量进行线性回归。 通过求解法线方程,梯度下降和随机(小批量)梯度下降确定的参数估计值 使用最速下降法和共轭梯度法求解线性方程组Ax = b 逻辑回归