microburst learning

Machine Learning is to study how computers simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their own performance. It is the core of artificial intelligence and the fundamental way to make computers intelligent. It is applied in various fields of artificial intelligence. It mainly uses induction, synthesis,

https://zhuanlan.zhihu.com/p/25185856 "Kaggle Instance Analysis" Titanic machine learning from disasterhttp://blog.csdn.net/wiking__acm/article/details/42742961 Titanic:machine Learning from disaster (Kaggle Data Mining contest)http://blog.csdn.net/han_xiaoyang/article/details/49797143 must-readHttps://github.com/yew1eb/DM-Competition-Getting-Started/tree/master/kaggle-titanichttps://duyiqi17.github.io/2017

Free and open source mobile deep The learning framework, deploying by Baidu. This is the simply deploying CNN on mobile devices with the low complexity and the high speed. It supports calculation on the IOS GPU, and is already adopted by the Baidu APP. size:340k+ (on ARM v7)Speed:40ms (for IOS Metal GPU mobilenet) or MS (for Squeezenet)Baidu Research and development of the mobile end of the deep learning fr

On-policy: The policy (value function) that generates the sample is the same as the policy (value function) used when updating parameters on the network. Typical for the Saras algorithm, based on the current policy directly perform a motion selection, and then update the current policy with this sample, so that the generation of sample policy and learning policy the same, the algorithm is On-policy algorithm. This method will encounter the contradicti

1. Research background and rationale 1958, Rosenblatt proposed Perceptron model (ANN)In 1986, Hinton proposed a deep neural network with multiple hidden layers (MNN)In the 2006, Hinton Advanced Confidence Network (DBN), which became the main frame of deep learning.Then, the efficiency of this algorithm is validated by Bengio Experiment 2.3 classes of depth learning models 2.1 Generating Deep modelDBN as the representative of the detailed introduction

) Discriminant analysis is mainly in the statistics over there, so I am not very familiar with the temporary find statistics Department of the Boudoir Honey made up a missed lesson. Here we are now learning to sell. A typical example of discriminant analysis is linear discriminant analysis (Linear discriminant analyses), referred to as LDA. (notice here not to be confused with the implied Dirichlet distribution (latent Dirichlet allocation), although

First, we look at the new progress of target detection from CVPR2016. The 2016 CVPR conference target detection method is mainly based on convolution neural network framework, Representative work has resnet (in faster r-cnn ResNet replacement Vgg), YOLO (regression detection framework), locnet (more accurate positioning), Hypernet (High level information of neural network is advantageous to the identification, the bottom characteristic is advantageous to the localization, the low layer character

Content Summary To now supervised learning has basically finished, this blog is mainly to write about the theory of machine learning, that is, when to use what learning algorithm, what kind of learning algorithms have what characteristics or advantages. At the time of fitting, how to choose the fitting model is actual

"Learn the basics of learning in simplified learning notes" 4. Reinforcement learning method without model-Monte Carlo algorithm Explain again what is no model. No model is the state transfer function, the return function does not know the situation.In the model-based dynamic programming method, which is based on model, including the strategy iteration method an

Tags: network security dvwa SQL BlindsMy way of learning, now 0 basis, is a small white, please daniel criticism! Write down this article, is a thought of their own collation, for reference only.Dvwa Login, first in Dvwa Security set a level of low, and then into SQL injection (blind), randomly enter a number to grab the packet, and then find URL injection points and cookies. (the tool used for grasping the package is fiddler, so we don't go into the

Naive Bayesian classifier is a set of simple and fast classification algorithms. There are many articles on the Internet, such as this one is relatively good: 60140664. Here, I'm going to sort it out as I understand it.In machine learning, we sometimes need to solve classification problems. That is, given a sample's eigenvalues (Feature1,feature2,... feauren), we want to know which category label the sample belongs to (Label1,label2,... Labeln). That

I recently read ng's deep learning tutorial and seeSelf-taught LearningSome concepts are unfamiliar. As a part of paying off the technical debt, I spent half an afternoon searching for several terms. If I want to use them later, I will try again. Supervised Learning has been discussed in the previous blog. Here we will mainly introduce migration learning and se