udemy machine learning

Course Description:This lesson focuses on the things you should be aware of in machine learning, including: Occam's Razor, sampling Bias, and Data snooping.Syllabus: 1, Occam ' s razor.2, sampling bias.3, Data snooping.1, Occam ' s Razor.Einstein once said a word: An explanation of the data should is made as simple as possible, but no simpler.There are similar sayings in software engineering:Keep It simple

After tossing the crawler and some interesting content, I recently in the R language for simple machine learning knowledge, the main reference is "machine learning-Practical Case Analysis" this book.This book is a rare, purely r language-based machine

SVM is a widely used classifier, the full name of support vector machines , that is, SVM, in the absence of learning, my understanding of this classifier Chinese character is support/vector machines, after learning, Only to know that the original name is the support vector/machine, I understand this classifier is: by the sparse nature of a series of support vecto

July algorithm December machine learning online Class---20th lesson notes---deep learning--rnnJuly algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com Cyclic neural networks Before reviewing the knowledge points:Full

IntroductionIn real life, we may unknowingly use a variety of machine learning algorithms every day. For example, when you use Google every time, it works well, and one of the important reasons is that a learning algorithm implemented by Google can "learn" how to rank pages. Every time you use a Facebook or Apple photo-processing app, they can automatically ident

2019 Machine Learning: Tracking the path of AI developmentHttps://mp.weixin.qq.com/s/HvAlEohfSEJMzRkH3zZtlwThe time has come to "guide" the "Smart assistant". Machine learning has become one of the key elements of the global digital transformation, and in the enterprise domain, the growth of

This article is from: http://blog.jobbole.com/56256/This is a hard-to-write article because I hope this article will inspire learners. I sat down in front of the blank page and asked myself a difficult question: what libraries, courses, papers, and books are best for beginners in machine learning.It really bothers me how to write and write nothing in the article. I have to think of myself as a programmer and a beginner of

"Python Machine learning and practice – from scratch to the road to Kaggle race" very basicThe main introduction of Scikit-learn, incidentally introduced pandas, NumPy, Matplotlib, scipy.The code of this book is based on python2.x. But most can adapt to python3.5.x by modifying print ().The provided code uses Jupyter Notebook by default, and it is recommended to install ANACONDA3.The best is to https://www.

structure as follows.What effect does this autoencoder have on machine learning?1) for supervised learning: This information-preserving NN's hidden layer structure + weight is a reasonable conversion of the original input, equivalent to learning the expression of data in the structure2) for unsupervised

train streaming data and make predictionsIn the following example, we train a perceptron to categorize the datasets of 20 news categories. This data set of 20 Web news sites collects nearly 20,000 news articles. This data set is often used for document classification and clustering experiments, and Scikit-learn provides an easy way to download and read datasets. We will train a perceptron to identify three news categories: Rec.sports.hockey, Rec.sport.baseball, and Rec.auto. Scikit-learn's perc

1. Training error: The error of the learner in the training set, also known as "experience Error"2. Generalization error: The error of the learner on the new sampleObviously, our goal is to get a better learner on a new sample, which is a small generalization error.3. Overfitting: The learner learns the training sample too well, leading to a decline in generalization performance (learning too much ...). Let me think of some people bookworm, reading de

converge or even diverge. .One thing worth noting:As we approach the local minimum, the guide values will automatically become smaller, so the gradient drop will automatically take a smaller amplitude, which is the practice of gradient descent. So there's actually no need to reduce the alpha in addition, we need a fixed (constant) learning rate α. 4. Gradient Descent linear regression (Gradient descent for Linear Regression) This is the method of us

: Random initialization Loop until convergence { Each State transfer count in the sample is used to update and R Use the estimated parameters to update V (using the value iteration method of the previous section) According to the updated V to re-draw } In step (b) We are going to do a value update, which is also a loop iteration, in the previous section we solved v by initializing v t

1. Nearest Neighbor Component analysis (NCA) algorithmAbove content reproduced from: http://blog.csdn.net/chlele0105/article/details/130064432. Metric LearningIn machine learning, the main purpose of dimensionality reduction of high dimensional data is to find a suitable low-dimensional space, in which the learning can be better than the original space performanc

say we have some data points, and now we use a straight line to fit these points, so that this line represents the distribution of data points as much as possible, and this fitting process is called regression.In machine learning tasks, the training of classifiers is the process of finding the best fit curve, so the optimization algorithm will be used next. Before implementing the algorithm, summarize some

Machine learning Notes (i)Today formally began the study of machine learning, in order to motivate themselves to learn, but also to share ideas, decided to send their own experience of learning to the Internet to let everyone share.Bayesian learningLet's start with an exampl

Original address: http://blog.csdn.net/google19890102/article/details/18222103The Extreme learning Machine ELM is a neural network algorithm proposed by Huangguang. The biggest feature of Elm is that the traditional neural network, especially the tow-layer feedforward neural Network (SLFNS), Elm is faster than the traditional learning algorithm.ELM is a new fast