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discriminative Learning and generative learning2011-12-08 10:47 1929 people read comments (2) favorite reports Variablesdependencies algorithm IncludeparametersexpressDiscriminative Learning algorithm is a kind of model input (X) output (Y) of the method of the relationship, simply like Chinese medicine, we only know with a number of drugs (Angelica, tiger bone ...) Can be made into a prescription, you c

The topic of this class is deep learning, the person thought to say with deep learning relatively shallow, with Autoencoder and PCA this piece of content is relatively close.Lin introduced deep learning in recent years has been a great concern: deep nnet concept is very early, just limited by the hardware computing power and parameter

[C ++/MFC quick learning series] sequence, mfc quick learning In order to understand the OCCT source code and lay the foundation for better use of OCCT, I plan to study C ++ and MFC in my spare time. By the way, I will give a simple tutorial, you can also record yourself and serve everyone. Because most of the time is usually used to write papers, the part of this tutorial will be updated occasionally, but

First, the perception deviceThe Perceptron, invented by Frank Rosenblatt in 1957 at the Cornell Aviation Laboratory, was inspired by the simulation of the human brain, which is a synapse (synapses) of information-processing neurons (neurons) cells and linked neuron cells.　　A neuron can be seen as a computational unit that processes one or more inputs into an output. A perceptron function is similar to a neuron: it accepts one or more inputs, processesThey then return an output. Neurons can be re

Machine learning Algorithms Study NotesGochesong@ Cedar CedroMicrosoft MVPThis series is the learning note for Andrew Ng at Stanford's machine learning course CS 229.Machine learning Algorithms Study Notes Series article Introduction3 Learning Theory3.1 Regularization and mo

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

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

The 2nd Chapter Perception MachineThe Perceptron is a linear classification model of class Two classification, whose input is the characteristic vector of an instance, and the perceptual machine corresponds to the separation of the examples into positive and negative two classes in the input space (feature space), which belongs to the discriminant model. A loss function is introduced based on the error classification, and the loss function is minimized by the gradient descent method, and the Per

The fate of life, strange and difficult to test.I thought the time was devoted to Java, but did not want to break into the hall of machine learning. That summer, the scorching sun, across 1000 kilometers to the strange city of wandering, I hope all this is worthwhile.I Java origin, slightly understand c,linux, database, technology slag slag.Hope every step of life is a new starting point, each step has a new state of mind.I have never heard of this in

Pig Data Structure Learning notes (1). Pig Data Structure Learning Pig's Data Structure Learning notes (1) Introduction to data structures and algorithms This section introduces: We have learned the basic C language series before. In this series, we will further learn about the data structure and Algorithm, which is very important and difficult to learn. The n

Original post: Http://opser.cz.cc /? P = 57 I. Reading Books Buy a pile of books and have a look. Reading books is typical of false learning. Reading a book is still reading it. It is just false learning, deceiving yourself, and comforting yourself that you are learning. Professional Books are well written, but most of them are written to people who already kn