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Drawing a learning curve is useful, for example, if you want to check your learning algorithm and run normally. Or you want to improve the performance or effect of the algorithm. Then the learning curve is a good tool. The learning curve can judge a

The use of neural networks to achieve autonomous driving, which means that the car through learning to drive themselves.It is a legend explaining how to realize automatic driving through neural network learning:The lower left corner is an image of

SVM is considered by many people to be the best algorithm for supervised learning, and I was trying to learn this time last year. However, the face of long formulas and the awkward Chinese translation eventually gave up. After a year, see Andrew to

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 data 11.1 what to do firstIn the next video, I'll talk about the design of the machine learning

In general, the relationship between recall and precision is as follows:1, if the need for a high degree of confidence, the precision will be very high, the corresponding recall rate is very low, 2, if the need to avoid false negative, the recall

This column (Machine learning) includes single parameter linear regression, multiple parameter linear regression, Octave Tutorial, Logistic regression, regularization, neural network, machine learning system design, SVM (Support vector machines

First, Introduction: 1, Phoenix is in the shared memory of the MapReduce implementation of the architecture. Its goal is to make programs execute more efficiently on multi-core platforms, and to keep programmers from having to care about concurrent

Job requirements and related introduction too much, directly affixed to the source code I wrote. However, there are some problems that are not fully understood. This can only be counted as an initial version. Program Source code: * * *

Independent component Analysis Contents [hide] 1 Overview 2 standard orthogonal ICA 3 topology ICA 4 Chinese-English translator overview Try to recall that in the introduction of sparse coding algorithms we want to learn a super complete base

Fine-tuning multilayer self-coding algorithm Contents [hide] 1 Introduction 2 general Strategy 3 using reverse propagation method for fine tuning 4 Chinese-English translator introduction Fine tuning is a common strategy in depth learning, which can

Sparse coded Contents [hide] 1 sparse 2 probability interpretation [based on 1996 Olshausen and Field theory] 3 Learning Algorithm 4 Chinese-English translator sparse coding Sparse coding algorithm is a unsupervised learning method, which is used to

From linear regression to neural network Mini-batchsgd Forward propagation calculation loss reverse propagation calculation gradient, updating parameters according to gradientTopological sort forward and reverse of graphs Class Computationalgraph

Self-learning Contents [hide] 1 Overview 2 feature learning 3 data preprocessing 4 unsupervised feature learning terms 5 Chinese and English If there is already a strong enough machine learning algorithm, one of the most reliable ways to achieve

The data and models used in this article can be downloaded from the CSDN resource page.Link:Network definition FileLST files for data linking and testingThis article mainly to the original code to organize, facilitate the call and training.The main

Part IV Generation Learning Algorithm So far, we have largely discussed the learning Algorithm model: P (y|x;θ), given x, the conditional probability distribution of Y. For example, the logistic regression model: P (y|x;θ), Where: Here the

Sparse coded self-coding expression Contents [Hide] 1 sparse encoding 2 topological sparse coding 3 sparse Coding Practice 3.1 sample batches as "Mini block" 3.2 Good s initial value 3.3 operational algorithm 4 Chinese-English

Exercise:self-taught Learning Contents [Hide] 1Overview 2Dependencies 3Step 1:generate The input and test data sets 4Step 2:train the sparse autoencoder 5Step 3:extracting features 6Step 4:training and testing the logistic regression

When we use the linear regression and logistic regression described in the previous blog, there is often an over-fitting (over-fitting) problem. The next definition is fitted below: overfitting (over-fitting):The so-called overfitting is: if we have

cs231n-assignment 1-q4-two-layer Neural Network Written: Guo Chengkun concept of Fanli slyned proofreading: Maoli He hui to and audit: cold Small Yang 1 Quests In this exercise, we will implement a fully connected neural network classifier and

Exercise:convolution and Pooling Contents [Hide] 1Convolution and Pooling 1.1Dependencies 1.2Step 1:load learned features 1.3Step 2:implement and test convolution and pooling 1.3.1Step 2a:implement convolution 1.3.2Step 2b:check your