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
Learning software Development should read the bookSource: The log of Li Yue Jia 1. C Language Promotion"C and Pointers"C Defects and pitfalls"C Expert Programming"This three book is a junior C programmer must read three books, but also fresh new employees have to study the textbook, very suitable for the newly graduated college students study2. C + + language promotion"Effective C + +: 55 specific practices
The basic thought of deep learningSuppose we have a system s, which has n layers (S1,... SN), its input is I, the output is O, the image is expressed as: I =>S1=>S2=>.....=>SN = o, if the output o equals input I, that is, input I after this system changes without any information loss (hehe, Daniel said, it is impossible.) In the information theory, there is a "message-by-layer-loss" statement (processing inequalities), the processing of a information
Deep learning--the artificial neural network and the upsurge of researchHu XiaolinThe artificial neural network originates from the last century 40 's, to today already 70 years old. Like a person's life, has experienced the rise and fall, has had the splendor, has had the dim, has had the noisy, has been deserted. Generally speaking, the past 20 years of artificial neural network research tepid, until the
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
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
research progress and prospect of deep learning in image recognitionDeep learning is one of the most important breakthroughs in the field of artificial intelligence in the past ten years. It has been a great success in speech recognition, natural language processing, computer vision, image and video analysis, multimedia and many other fields. This paper focuses o
deep Learning network uses a sophisticated algorithm, with millions of simulated neurons, with billions of connections, but their training methods are the same. Rosenblatt predicted that Perceptrons would soon being capable of feats like greeting people by name, and he idea became a l Inchpin of the nascent field of artificial intelligence. Work focused in making perceptrons with more complex networks, arr
Transferred from: http://blog.csdn.net/zouxy09/article/details/8775488
Because we want to learn the characteristics of the expression, then about the characteristics, or about this level of characteristics, we need to understand more in-depth point. So before we say deep learning, we need to re-talk about the characteristics (hehe, actually see so good interpretation of the characteristics, not put here a l
0. OriginalDeep learning algorithms with applications to Video Analytics for A Smart city:a Survey1. Target DetectionThe goal of target detection is to pinpoint the location of the target in the image. Many work with deep learning algorithms has been proposed. We review the following representative work:SZEGEDY[28] modified the
foundation, followed by the details of the deep, and further understand the essence of C + +. "effective C + +" can be seen as a C + + troubleshooting manual; for the C + + language level in-depth things, error-prone knowledge points, in this book can find a satisfactory answer, suitable for multiple tastes. "c + + must know will" "more effective C + +" as to effective C + + did not finish the supplement,
-level Click logs can be used to obtain an estimate model through a typical machine learning process, thus increasing the CTR and rate of return on internet advertising;Personalized Recommendations, or through a number of machine learning algorithms to analyze various purchases on the platform, browse and collect logs, get a recommendation model to predict your favorite products.Depth
1. JavaJava programming language (Third edition) --- four famous Java books ---- James Gosling (father of Java)Java programming ideology (version 2nd) ---- four famous Java books ---- Bruce EckelJava programming ideology (version 3rd) ---- four famous Java books -------------- Bruce EckelJava 2 core technology Volume I: Basic knowledge (7th) --- four famous Java books ----- Cay horstmannJava 2 core technology Volume II: advanced features (original book
Deep understanding of Java Virtual Machine-learning notes and deep understanding of Java Virtual Machine
JVM Memory Model and partition
JVM memory is divided:
1.Method Area: A thread-shared area that stores data such as class information, constants, static variables, and Code Compiled by the real-time compiler loaded by virtual machines.
2.Heap:The thread-shared
Deep Learning notes ------ windows system for Linux-Ubuntu14.04 dual system installation notes (a), deep linux dual system installation notes
Currently, deep learning is widely used in target detection and Classification Research, and most Neural Network frameworks (such as
Deep learning is now a hot concept in machine learning, but the concept has become a bit of a myth as it is reproduced in various media: for example, deep learning can be thought of as a machine learning method that simulates the
learning algorithms which are widely used in image classification in the industry and knn,svm,bp neural networks.
Gain deep learning experience.
Explore Google's machine learning framework TensorFlow.
Below is the detailed implementation details.
First, System design
In this project, 5 algorithms for experiments are K
Ext.: http://mp.weixin.qq.com/s?__biz=MzAwNDExMTQwNQ==mid=209152042idx=1sn= Fa0053e66cad3d2f7b107479014d4478#rd#opennewwindow1. Deep Learning development Historydeep Learning is an important breakthrough in the field of artificial intelligence in the past ten years. It has been successfully used in many fields such as speech recognition, natural language processi
is worth mentioning that the middle layer added a lot of softmax classifier, to prevent overfitting, that is: When the inception network, the branches of the same output, in order to make full use of the neural network structure, in the middle layer is the output, the final comparison of the output results, In order to find the best output of the corresponding structure.
8.Using Open-source Implementation
We can look for existing open source files from GitHub in the process of training the r
Self-learning is a softmax classifier connected by a sparse encoder. As shown in the previous section, the training is performed 400 times with an accuracy of 98.2%.
On this basis, we can build our first in-depth Network: stack-based self-coding (2 layers) + softmax Classifier
In short, we use the output of the sparse self-Encoder as the input of a higher layer of sparse self-encoder.
Like self-learning, i
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