rnn machine learning

phenomenon of gradient dispersion and gradient explosion, to avoid a W from start to finish, with a certain common sense memory abilityThe most widely used and successful RNN?2.1 Cell State (unit status)?1, you can save a state for a long time, the cell state value through the forget GAT (multiplication in the picture) control to preserve how much "old" status,2, layer turns input dimension x into output dimension h?2.2 Forget/input UnitAs for Yes [0

RNN Encoder-decoder is proposed for machine translation.Encoder and decoder are two rnn, which are put together for parameter learning to maximize the conditional likelihood function.Network structure:Note the input statement is not necessarily the same length as the output statement.At the encoder end, the hidden stat

1. The main task accomplished was the ability to translate English into French, using a encoder-decoder model, in which the sequence was transformed into a vector in the encoder RNN model. In decoder, a vector is transformed into an output sequence, and encoder-decoder can be used to add sequential information between words and words.2. Another task is to express the sequence as a vector, using vectors to clearly see that semantically similar words ga

Learning notes TF020: Sequence Annotation, handwritten lowercase letter OCR dataset, bidirectional RNN, tf020rnn Sequence labeling, which predicts a category for each frame of the input sequence. OCR (Optical Character Recognition ). MIT oral Systems Research Group Rob Kassel collection, Stanford University Artificial Intelligence Laboratory Ben Taskar pre-processing OCR dataset (http://ai.stanford.edu /~ B

This paper mainly introduces the sentence matching method based on the bidirectional rnn (LSTM, GRU) and attention model, which is used to match the sentences with Word2vec and Doc2vec, and the method of sentence matching based on the traditional machine learning method. First look at what is called sentence to match: Sentence pair matching (sentence Pair Matchin

, T time has not learned the parameters of the t-n moment. Of course, there are a number of ways to solve this problem, such as LSTMS is specifically to deal with this problem, there are some methods, such as the design of a better initial parameters and replace the activation function (such as switch to Relu activation function). The above is the classic RNN model and the derivation process, in recent years rely on

latest progress in deep learning--the anti-neural network. It mainly includes the idea of resisting the neural network and two specific Gan networks, the deep convolution countermeasure Generation Network (Dcgan) and the image translation (PIX2PIX) model. The knowledge points involved include generator G, discriminant D, deconvolution, u-net and so on. ... 10th Automatic Machine

Organized Links: https://www.zhihu.com/question/41631631Source: KnowCopyright belongs to the author. Commercial reprint please contact the author for authorization, non-commercial reprint please specify the source.Adjusted for almost 1 years rnn, deeply felt that deep learning is an experimental science, the following are some of the alchemy experience. will continue to be added later. Where there is a prob

Keras is a Theano and TensorFlow-compatible neural network Premium package that uses him to component a neural network more quickly, and several statements are done. and a wide range of compatibility allows Keras to run unhindered on Windows and MacOS or Linux.Today to compare learning to use Keras to build the following common neural network: Regression RNN regression Classification CN

RNN model of deep learning--keras training RNN principle: (Recurrent neural Networks) cyclic neural network. It interacts with each neuron in the hidden layer and is able to handle the problems associated with the input and back. In RNN, the output from the previous moment is passed along with the input of the next mo

This set of notes is followed by the July algorithm May in-depth study of learning and recorded, mainly remember me to learn machine learning when some of the concepts are more vague, specific courses refer to the July Judge network:http://www.julyedu.com/ RNN: Using neural networks to process the state and model of se

/ * copyright notice: Can be reproduced arbitrarily, please indicate the original source of the article and the author information . */Author: Zhang JunlinThe outline is as follows:1.RNN2.LSTM3.GRN4.Attention Model5. Application6. Discussion and thinkingSweep attention Number: "The Bronx Area", deep learning in natural language processing and other intelligent applications of technical research and Popular science public number.Deep

[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning) PDF Video Keras Example application-handwriting Digit recognition Step 1

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 st

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 Support vector machine), clust

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 Support vector machine), clust

Objective:When looking for a job (IT industry), in addition to the common software development, machine learning positions can also be regarded as a choice, many computer graduate students will contact this, if your research direction is machine learning/data mining and so on, and it is very interested in, you can cons

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of

Objective Machine learning is divided into: supervised learning, unsupervised learning, semi-supervised learning (can also be used Hinton said reinforcement learning) and so on. Here, the main understanding of supervision and unsu

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 Support vector machine), clust