lstm

This article is Kaldi official website about NNET3 translation: Brief Introduction This article covers the latest nnet3. To understand all the DNN in Kaldi, such as the Karel version, refer to Deep neural Networks in Kaldi. Nnet3 is created to support more common kinds of networks in a more natural way without the need for real coding, not just feedback networks (such as RNN and LSTM). As with Nnet2 support for multiple-machine multi-GPU parallelism

many applications in the fields of speech recognition, music synthesis, chat robot, machine translation and natural language understanding. Through this course, you will: understand how to build and train a cyclic neural network (RNN), and some widely used variants, such as GRU and lstm, that can apply sequence models to natural language problems, including literal synthesis. The ability to apply sequence models to audio applications, including speec

event description task. Finally, we demonstrate that activitynet captions can be used to study video retrieval and event positioning. Paper Address: Http://openaccess.thecvf.com/content_ICCV_2017/papers/Krishna_Dense-Captioning_Events_in_ICCV_2017_paper.pdf About this paper, the battalion commander does not do too much analysis, the following is the analysis of two students know, for reference. Original address: https://www.zhihu.com/question/59639334/answer/167555411 Know the answer to th

-scale corpora; 3 The effect of the NIC is beyond State-of-art. Bleu on the Pascal DataSet, the score contrast was 59/25,human scored 69. Reference: The Chinese translation of this thesis: http://www.jianshu.com/p/3330a56f0d5e Note: Although the source code for this paper was not found, Moses Soh's article implements a similar architecture. Moses Soh's article "Learning cnn-lstm Architectures for Image" implements a similar architecture. Article Downl

explosion problems, and lstm and GRU for solving problemsCode: Using TensorFlow to actually demonstrate the training and use of a task This is the first part: Gitbook Initial Source Address: Recurrent neural network--Introduction There are a lot of dynamic diagrams, please click to watch, can not see the words suggested to go above the Gitbook address reading recurrent neural network--Introduction of timing prediction Code Illustration 3 has shown ho

answer system, the input sequence is the problem, and the output sequence is the answer. In order to solve the problem of seq2seq, some people put forward the Encoder-decoder model, namely the coding-decoding model. The so-called code, is to convert the input sequence into a fixed-length vector, decoding, is to convert the previously generated fixed vector into an output sequence. Of course, this is only a general idea, the implementation of the time, the encoder and decoder are not fixed,

This article was reproduced from: https://zhuanlan.zhihu.com/p/31941650 These days in See Mo Fan teacher TensorFlow (hereinafter referred to as TF) video, was a few size dizzy, read some information, say their own understanding, and we discussed. I tf small white, still in the study, please many advice. The contents of this article are personal views, such as errors, please point out in time, thank you Words don't say much, start to masturbate: First a few key size:input_size of TF: Input data

development. " algorithmic engineer" or "Algorithmic application Engineer" Around a lot of friends are from the start of the service, usually solve a lot of problems are engineering-related problems. I know the bat and the students working abroad, although the "algorithm engineer" related to the title, but the vast majority are directly using the company or the existing computing platform in the industry, the existing algorithm for use, and even a small part of the current algorithm will be lar

arithmetic coding.A brief overview of data compression. Description data compression = encode + model. Then it shows that coding is a problem that has been solved, and the model is more of an AI problem. Refer to the book of the DonPpm. Use Python to reproduce a ppm to compress the brown corpus (or the entire corpus so that it compares to the 300M 233). Notice the use of the NLTK in the function, with N-gram instead of the above, using freqdist to do statistics. (Sense of complexity to explode)

out The common denominator of the two articles is that they are all implemented with SEQ2SEQ. The LSTM model structure is: The details can go directly to the above text, where the PO out of the model stage to establish a brief flow diagram and process description: First of the original data 300w chat to do a preprocessing, that is, cut words, divided into questions and answers. Then we use Word2vec to train the word vector to generate the binary word

http://blog.csdn.net/u011274209/article/details/53384232 principle: article Source: Memory Networks, answering Reading comprehension Using Memory For many neural network models, there is a lack of a long memory component for easy reading and writing. As Rnn,lstm and its variants GRU used a certain memory mechanism. These memories are too small for the authors of memory networks, because the state, the output of the cell, and its weights are all emb

-notes for the "Deep Learning book, Chapter Sequence modeling:recurrent and recursive Nets. Meta Info:i ' d to thank the authors's original book for their great work. For brevity, the figures and text from the original book are used without. Also, many to Colan and Shi for their excellent blog posts on Lstm, from which we use some figures. Introduction Recurrent neural Networks (RNN) are for handling data. Rnns share parameters across different positi

.Simple examples to introduce pytorch Https://github.com/jcjohnson/pytorch-examples.git Pytorch tutorials provided by users, through a number of examples of ways to explain the basic principles of pytorch. The content involves NumPy, automatic derivation, parameter optimization, weight sharing and so on. 2 Getting Started example 1.Ten minutes Pytorch Tutorial Https://github.com/SherlockLiao/pytorch-beginner.git Know the source of the "10-minute Learning Pytorch" series tutorial. 2.Official Pyto

method has not heard that people use the affirmation effect is not ^_^.4, if the sentence itself can form a 1-dimensional space vector, the subject of each word has a vector of dimensions. It's natural to think that we can use convolution to solve this problem.(iv) The model of deep learning to buildQuestion: Since we want to use a deep learning model, then how do we let the model identify our initial data.We can do this:1, each sentence is convolution into a vector, using this vector to find t

-11/2826192 12.The unreasonable effectiveness of recurrent neural Networks. The amazing effectiveness of cyclic neural networks by Andrej karpathy Link: http://karpathy.github.io/2015/05/21/rnn-effectiveness/translation: https:// zhuanlan.zhihu.com/p/22107715 13.Understanding lstm Networks Understanding Long Term Memory Network (LSTM Networks) by Colah Link: http://colah.github.io/posts/2015-08- understandi

short-term. H is used immediately to the output, C has been passed in step. It could be a two-step previous value, or it could be a 100-step previous value, that is, the dimension in C is a long-ago state, or a very near state. Each a block is called a layer Lstm:forget/input UnitH (t-1) and X as two input "" means multiply and add. F control how much to forget, Lstm:update cellFT: Controls the percentage of retention states. Shows the use of long memory or short memory.It: Ensure the CT can be

When using Pytorch's RNN module, it is sometimes unavoidable to use pack_padded_sequence and pad_packed_sequence, when using two-way RNN, you must use Pack_padded_seque NCE ! Otherwise, the Pytorch is unable to obtain the length of the sequence, and it does not correctly calculate the results of the bidirectional rnn/gru/lstm. However, there is a problem when using pack_padded_sequence, that is, the length of the input mini-batch sequence must be orde

generation, and chat bots.To technology like me: In fact, the architecture of neural translators is very diverse. The conventional RNN, which was used at first, was later upgraded to two-way RNN, where the translator not only had to consider the words before the source word, but also the words to be considered thereafter. It's much more efficient. Then it uses multi-layer RNN with LSTM unit, which can realize the long-term storage of translation cont

# #tensorflow简单介绍:TensorFlow? is a open source software library for numerical computation using data Flow graphs.https://www.tensorflow.org/TensorFlow is Google's second generation of AI learning systems based on Distbelief, and its nomenclature derives from its own operating principles. Tensor (tensor) means that n-dimensional arrays, flow (flow) means that based on the calculation of the flow graph, the TensorFlow is the calculation process of the tensor from one end of the image to the other.

-processing can try to format the recognition results, such as in the image of the layout of what, for example, a chestnut, an image, the left half of the text and the right half of the text has no relationship, In the process of segmentation, the first line of the left half of the recognition result is followed by the first line of the right half and so on. 1.2.OCR Different technical subdivision slightly different, but the approximate principle is the same. That is, the main technical process