udacity machine learning

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

"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.

Machine learning system Design (Building machines learning Systems with Python)-Willi Richert Luis Pedro Coelho General statementThe book is 2014, after reading only found that there is a second version of the update, 2016. Recommended to read the latest version, the ability to read English version of the proposal, Chinese translation in some places more awkward

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

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

, there are n single classifiers, each single classifier has an equal error rate, and the single classifier is independent of each other, error rate is irrelevant. With these assumptions, we can calculate the error probability of the integration model:If n=11, the error rate is 0.25, to integrate the result prediction error, at least 6 single classifier prediction results are incorrect, the error probability is:Integration result error rate is only 0.034 oh, much smaller than 0.25. The inheritan

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data11.1 what to do firstThe next video will talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when desi

solving the parameters can be accomplished by the optimization algorithm. In the optimization algorithm, the gradient ascending algorithm is the most common one, and the gradient ascending algorithm can be simplified to the random gradient ascending algorithm.2.2 SVM (supported vector machines) Support vectors machine:Advantages: The generalization error rate is low, the calculation cost is small, the result is easy to explain.Cons: Sensitive to parameter adjustment and kernel function selectio

This section describes the core of machine learning, the fundamental problem-the feasibility of learning. As we all know about machine learning, the ability to measure whether a machine learni