Machine learning is a branch in computer science that studies the design of algorithms that can learn. Typical machine learning tasks are concept learning, function learning or “predictive modeling”, clustering and finding predictive patterns. These…
https://www.quora.com/How-do-I-learn-machine-learning-1?redirected_qid=6578644 How Can I Learn X? Learning Machine Learning Learning About Computer Science Educational Resources Advice Artificial Intelligence How-to Question Learning New Things Lea…
In my last article, I stated that for practitioners (as opposed to theorists), the real prerequisite for machine learning is data analysis, not math. One of the main reasons for making this statement, is that data scientists spend an inordinate amoun…
https://www.quora.com/How-do-I-learn-mathematics-for-machine-learning How do I learn mathematics for machine learning? Promoted by Time Doctor Software for productivity tracking. Time tracking and productivity improvement software with screenshots…
Targeted learning methods build machine-learning-based estimators of parameters defined as features of the probability distribution of the data, while also providing influence-curve or bootstrap-based confidence internals. The theory offers a general…
INDEX Introducing ML Framing Fundamental machine learning terminology Introducing ML What you learn here will allow you, as a software engineer, to do three things better. First, it gives you a tool to reduce the time you spend programming. Second, i…
转自:机器学习(Machine Learning)&深度学习(Deep Learning)资料 <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最…
Week1: Machine Learning: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Supervised Learning:We alr…
the main steps: 1. look at the big picture 2. get the data 3. discover and visualize the data to gain insights 4. prepare the data for machine learning algorithms 5. select a model and train it 6. fine-tune your model 7. present your solution 8. laun…
Recommended Books Here is a list of books which I have read and feel it is worth recommending to friends who are interested in computer science. Machine Learning Pattern Recognition and Machine Learning Christopher M. Bishop A new treatment of classi…
Week 1: Machine Learning: A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E. Supervised Learning:We al…
转载:http://dataunion.org/8463.html?utm_source=tuicool&utm_medium=referral <Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智…
转载:http://www.jianshu.com/p/b73b6953e849 该资源的github地址:Qix <Statistical foundations of machine learning> 介绍:<机器学习的统计基础>在线版,该手册希望在理论与实践之间找到平衡点,各主要内容都伴有实际例子及数据,书中的例子程序都是用R语言编写的. <A Deep Learning Tutorial: From Perceptrons to Deep Networks>…
##Advice for Applying Machine Learning Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the le…
<Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最新版本<神经网络与深度学习综述>本综述的特点是以时间排序,从1940年开始讲起,到60-80…
In recent years, Kernel methods have received major attention, particularly due to the increased popularity of the Support Vector Machines. Kernel functions can be used in many applications as they provide a simple bridge from linearity to non-linear…
1 Unsupervised Learning 1.1 k-means clustering algorithm 1.1.1 算法思想 1.1.2 k-means的不足之处 1.1.3 如何选择K值 1.1.4 Spark MLlib 实现 k-means 算法 1.2 Mixture of Gaussians and the EM algorithm 1.3 The EM Algorithm 1.4 Principal Components…
7 Machine Learning System Design Content 7 Machine Learning System Design 7.1 Prioritizing What to Work On 7.2 Error Analysis 7.3 Error Metrics for Skewed Classed 7.3.1 Precision/Recall 7.3.2 Trading off precision and recall: F1 Score 7.4 Data for ma…
<Brief History of Machine Learning> 介绍:这是一篇介绍机器学习历史的文章,介绍很全面,从感知机.神经网络.决策树.SVM.Adaboost到随机森林.Deep Learning. <Deep Learning in Neural Networks: An Overview> 介绍:这是瑞士人工智能实验室Jurgen Schmidhuber写的最新版本<神经网络与深度学习综述>本综述的特点是以时间排序,从1940年开始讲起,到60-80…
绘制了一张导图,有不对的地方欢迎指正: 下载地址 机器学习中,特征是很关键的.其中包括,特征的提取和特征的选择.他们是降维的两种方法,但又有所不同: 特征抽取(Feature Extraction):Creatting a subset of new features by combinations of the exsiting features.也就是说,特征抽取后的新特征是原来特征的一个映射. 特征选择(Feature Selection):choosing a subset of all…
