yolo deep learning

Neural network and deep learning the book has been read several times, but each time there will be a different harvest.The paper of DL field is changing rapidly. There's a lot of new idea coming out every day, I think. In-depth reading of classic books and paper, you will be able to find Remian open problems. So there's a different perspective.Ps:blog is a summary of important contents in the main extract b

), matrix decomposition (matrices factorization). In applying the compression perception process, we find that most of the signals themselves are not sparse (that is, the expression in the natural base is not sparse). But after a proper linear transformation is sparse (that is, the other group of bases (basis) or frames (frame, I do not know how to translate) are sparse). such as harmonic extraction (harmonic retrieval), the time domain signal is not sparse, but in the Fourier domain signal is

Note: This page is a guided page, followed by 7 major tutorials and some high-level examples, step by step to explain deep learning.The tutorials here will provide you with some of the most important deep learning algorithms, and will also tell you how to use Theano to run them. Theano is a Python class library that helps you write

Deep Learning paper notes (vii) Visualization of high-level features in depth networks Zouxy09@qq.com Http://blog.csdn.net/zouxy09 I usually read some papers, but the old feeling after reading will slowly fade, a day to pick up when it seems to have not seen the same. So want to get used to some of the feeling useful papers in the knowledge points summarized, on the one hand in the process of finishing, t

Deep Learning Source code Collection-Continuous update ... Zouxy09@qq.com Http://blog.csdn.net/zouxy09 Collected some source code for deep learning. The main is MATLAB and C + +, of course, there are python. Put it here and follow up with new updates that will continue. The table below is also welcome to be available

Deep Learning Chinese Translation In the help of many netizens and proofreading, the draft slowly became the first draft. Although there are many problems, at least 90% of the content is readable and accurate. As far as possible, we kept the meaning of the original book Deep learning and kept the statement of the origi

Nowadays, AI is getting more and more attention, and this is largely attributed to the rapid development of deep learning. The successful cross-border between AI and different industries has a profound impact on traditional industries.Recently, I also began to keep in touch with deep learning, before I read a lot of ar

The following is only my personal knowledge, not to mention please PAT.(At present, I only see some deep learning review and Tom Mitchell's book "Machine Learning" in the Neural network chapter, the understanding is limited. Feel 3\4 speak generally, reluctantly a look. The fifth chapter is purely to make notes, really bad expression, do not understand or look at

Deep Learning Library packages Theano, Lasagne, and TensorFlow support GPU installation in Ubuntu With the popularity of deep learning, more and more people begin to use deep learning to train their own models. GPU training is muc

First spit groove, deep learning development speed is really fast, deep learning framework is gradually iterative, it is really hard for me to engage in deep learning programmer. I began three years ago to learn

Second lecture: Simple word vector representation: Word2vec, Glove (easy word vector representations:word2vec, Glove)Reprint please specify the source and retention link "I love Natural Language processing": http://www.52nlp.cnThis article link address: Stanford University deep Learning and Natural language processing second: Word vectorRecommended Reading materials: paper1:[distributed representat

weight ratio, if the 10^-3 around is better, if too small, the learning speed will be relatively slow, too big words will be unstable.Initialization weights: at the beginning of the random initialization weightsThe initialization method mentioned here will not be particularly clear or not written. However, it is said that for shallow network simpler initialization method, the network can also work normally, but for

Document directory 1.1 how to restrict the use of the Polman machine (RBM) 1.2 restricted Polman machine (RBM) Energy Model 1.3 from energy model to probability 1.4 Maximum Likelihood 1.5 Sampling Method Used 1.6 introduction to Markov Monte Carlo References RBM for deep learning Reading Notes Statement: 1) I saw a statement from other blogs such as @ zouxy09, and the old man copied it. 2) This blo

Deep learning articles and code collections for text categorizationOriginal: franklearningmachine Machine Learning blog 4 days ago [1] convolutional neural Networks for sentence classificationYoon KimNew York UniversityEMNLP 2014http://www.aclweb.org/anthology/D14-1181This article mainly uses CNN to classify sentences based on pre-trained word vectors. The auth

time series signals. CNNs is the first learning algorithm to truly successfully train a multi-layered network structure. It uses spatial relationships to reduce the number of parameters that need to be learned to improve the training performance of the general Feedforward BP algorithm. CNNs as a deep learning architecture is proposed to minimize the preprocessin

Source: Michael Nielsen's "Neural Network and Deep learning", click the end of "read the original" To view the original English.This section translator: Hit Scir undergraduate Wang YuxuanDisclaimer: If you want to reprint please contact [email protected], without authorization not reproduced. Using neural networks to recognize handwritten numbers How the inverse propagation algorithm wor

Python to do deep learning caffe design CombatEssay background: In a lot of times, many of the early friends will ask me: I am from other languages transferred to the development of the program, there are some basic information to learn from us, your frame feel too big, I hope to have a gradual tutorial or video to learn just fine. For learning difficulties do no

Preface: Recently, I intend to learn some theoretical knowledge of deep learing in a slightly systematic way, and intend to use Andrew Ng's Web tutorial Ufldl Tutorial, which is said to be easy to read and not too long. But before this, or review the basic knowledge of machine learning, see Web page: http://openclassroom.stanford.edu/MainFolder/CoursePage.php?course=DeepLearning. The content is actually ver

companies want to go to the company to brush the work card, but here we do not need it, using face recognition, see what I can do. When I come close, it will recognize my face and then say "Welcome" (Andrew NG), I can pass without a work-cards. Let's take a look at another situation, next to Lin Yuanqing, IDL (Baidu Deep Learning Laboratory) Director, he led the development of the face recognition system,

From self learning to deep network In the previous section, we used the self encoder to learn the characteristics of input to the Softmax or logistic regression classifier. These features are only learned using data that is not annotated. In this section, we describe how to fine-tune these features using the annotated data for further refinement. If you have a large number of tagged data, you can significan