learning pyspark

Video Learning Website learning duration real-time recording-performance optimization practices, real-time performance optimization I. Application Scenario Description The system provides services for teachers to learn online. The video learning website supports online video learning for teachers. During video

Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduction, anomaly detection, large-scale machine

some time ago on the Internet to see the Coursera Open Classroom Big Machine learning Cornerstone Course, more comprehensive and clear machine learning needs of the basic knowledge, theoretical basis to explain. There are several more important concepts and ideas in foundation, first review, and then open the follow-up techniques to learn and summarize the course.1. VC Dimension (VC dimension, very importan

Original: http://blog.csdn.net/abcjennifer/article/details/7797502This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduction, anomaly detection, large-scale machine

Learning notes TF042: TF. Learn, distributed Estimator, deep learning Estimator, tf042estimator TF. Learn, an important module of TensorFlow, various types of deep learning and popular machine learning algorithms. TensorFlow official Scikit Flow project migration, launched by Google employee Illia Polosukhin and Tang Y

Source: http://wanghaitao8118.blog.163.com/blog/static/13986977220153811210319/Google's deep-mind team published a bull X-ray article in Nips in 2013, which blinded many people and unfortunately I was in it. Some time ago collected a lot of information about this, has been lying in the collection, is currently doing some related work (want to have a small partner to communicate).First, related articlesOn the DRL, this aspect of the work should be with the deep

Simple examples are used to understand what machine learning is, and examples are used to understand machine learning. 1. What is machine learning? What is machine learning? Different people may have different understandings about this issue. In my personal opinion, to describe machine

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 learning knowledge, covering 11 cases. Divided into 12 chapters. Both the author's notes and the code sections ar

I find myself coming back to the same few pictures when explaining basic machine learning concepts. Below is a list I find most illuminating.1. Test and Training error: Why lower training error was not always a good thing:esl figure 2.11. Test and training error as a function of model complexity.2. Under and overfitting: PRML figure 1.4. Plots of polynomials has various orders M, shown as red curves, fitted to the data set generated by the green curve

http://blog.csdn.net/pipisorry/article/details/44119187Machine learning machines Learning-andrew NG Courses Study notesMachine Learning System DesignPrioritizing what do I do on priorityError analysisError Metrics for skewed Classes Error metrics with biased classesTrading Off Precision and recall weigh accuracy and recall rateData for machines

Chapter One introduction to Deep learning 1. Artificial sometimes not very good to extract the characteristics of the entity, then there is an automatic way. Yes, one of the key problems in the deep learning solution is to automatically combine simple features into more complex features and use these combination features to solve problems. 2. Depth learning is a

Learning notes TF057: TensorFlow MNIST, convolutional neural network, recurrent neural network, unsupervised learning, tf057tensorflow MNIST convolutional neural network. Https://github.com/nlintz/TensorFlow-Tutorials/blob/master/05_convolutional_net.py.TensorFlow builds a CNN model to train the MNIST dataset. Build a model. Define input data and pre-process data. Read the data MNIST to obtain the training