edx learning

A simple record of the learning process, stay here to find it conveniently laterOne, register1,esp point to the top of the stackEIP points to the instruction to be executedThere are Eax,ecx,edx,ebx,esp,ebp,esi,edi and EIP, which are all referred to as 32-bit registers.AX contains a value of EAX after 4 digits. can also continue to be divided into Al and Ah2, Flag RegisterHere the signs are divided into C,p,

Original address: http://www.cnblogs.com/cyruszhu/p/5496913.htmlDo not use for commercial use without permission! For related requests, please contact the author: [Email protected]Reproduced please attach the original link, thank you.1 BasicsL Andrew NG's machine learning video.Connection: homepage, material.L 2.2008-year Andrew Ng CS229 machine LearningOf course, the basic method does not change much, so the courseware PDF downloadable is the advanta

mainly explain the knowledge of linear algebra, using the Octave library. Caltech learning from data at the California Institute of Technology: You can take this course on edx, which is explained by Yaser Abu-mostafa. All course videos and materials are available on the California Institute of Technology website. Similar to the Stanford curriculum, you can schedule your studies to complete y

Deep Learning (depth learning) Learning notes finishing Series[Email protected]Http://blog.csdn.net/zouxy09ZouxyVersion 1.0 2013-04-08Statement:1) The Deep Learning Learning Series is a collection of information from the online very big Daniel and the machine

Learning notes embedded in GCC Assembly I-- The first addition calculator for mixed EncodingAuthor: shellex.Shellex.cn blog.csdn.net/shellex All Rights ReservedI wrote a simple piece of code: # Include Int main (){Int in1 = 0, in2 = 0, out = 0;Printf ("plz input 2 number like this: (X1 + x2)/n ");Scanf ("% d + % d", in1, in2 );ASM volatile ("Add % 1, % 0/n/t""Add % 2, % 0/n/t""NOP/n/t": "= R" (out): "R" (in1), "R" (in2):);Printf ("% d + % d = % d./

supervised learning : In short, given a certain training sample (it is important to note that the sample is both data and data corresponding to the results), using this sample training to get a model (can be said to be a function), and then use this model to map all the input to the corresponding output, The output is then simply judged so that the problem of classification (or regression) is achieved. Simply make a distinction, the classification is

For the performance of four different algorithms in different size data, it can be seen that with the increase of data volume, the performance of the algorithm tends to be close. That is, no matter how bad the algorithm, the amount of data is very large, the algorithm can perform well.When the amount of data is large, the learning algorithm behaves better:Using a larger set of training (which means that it is impossible to fit), the variance will be l

I don't know. As a complete Windows platform under the less professional software engineering students, see the "Accelerated C + +" source code, the first reaction is: Oh! I should use make to generate project files. Then I happily use AOL to start searching for relevant information.And then the egg! I must have been possessed by some strange creature. I should import files directly with VS Create Project. Then ... ctrl+f5. How perfect.But...... Following:"Tutorial" from the cloud-wind Big blog

IntroductionThe systematic learning machine learning course has benefited me a lot, and I think it is necessary to understand some basic problems, such as the category of machine learning algorithms.Why do you say that? I admit that, as a beginner, may not be in the early stage of a learning object has a comprehensive

Transferred from: http://blog.csdn.net/zouxy09/article/details/8775518 Well, to this step, finally can talk to deep learning. Above we talk about why there are deep learning (let the machine automatically learn good features, and eliminate the manual selection process. As well as a hierarchical visual processing system for reference people, we get a conclusion that deep

54975700 push game.00579754; ASCII ". \ datapool \ gmdp_characterdata.cpp" 0044b2a7 68 48975700 push game.00579748; ASCII "ct_monster" 0044b2ac 68 20975700 push game.00579720; ASCII "character must not % s, (File: % s line: % d )" 0044b2b1 ffd7 call EDI 0044b2b3 83c4 10 Add ESP, 10 0044b2b6 8b4e 04 mov ECx, dword ptr ds: [ESI + 4] 0044b2b9 8b11 mov edX, dword ptr ds: [ECx] 0044b2bb ff92 14010000 call dword ptr ds: [

sixth week. Design of learning curve and machine learning system Learning Curve and machine learning System Design Key Words Learning curve, deviation variance diagnosis method, error analysis, numerical evaluation of machine learning