LSTM Study and Summary 1

Source: Internet
Author: User

The long short-term memory network lstm (long, shorter) is not a complete model in itself, but is mainly an improvement on the RNN hidden layer. Therefore, the RNN network is the RNN network that uses the LSTM unit. The lstm is ideal for dealing with issues that are highly correlated with time series, such as machine translation, dialog generation, encoding and decoding, and graphics conversion.

Description: Word2vec (cbow,skip-gram), Gru,word embedding (Word vector), MDP (Markov decision Process), deep reinforcement learning,dqn, etc.

Reference documents:

[1] lstm implementation of the explanation:

[2] Char-rnn:

[3] In layman lstm neural network:

[4] Learning to read with recurrent neural networks: recurrent-neural-networks-deep-net-optimization-lstm/

[5] Understanding LSTM Network:

[6] The derivation of the BP algorithm for Deep learning (additional rnn,lstm deduction description):

[7] LSTM Networks for sentiment analysis:

[8] How to evaluate the recent comparison of fire lstm? :

[9] Long short-term memory:tutorial on LSTM recurrent networks:

[Ten] Caffe-lstm:

[One] lstm introduction and mathematical deduction:

Lstm and sentiment analysis:

[13] What are the lstm (Long short term Memory) and RNN (recurrent) network tutorials? :

[14] Deep learning materials Daquan:

[15] Recent internet-swept deep dream AI image recognition software:

[16] Deep learning: A new engine driving the development of the NLP field:

[17] Inventory of 8 Most inspiring deep learning applications:

[DQN] from getting started to giving up (DQN and reinforcement learning):

[19] Depth Enhancement Learning DRL column:

LSTM Study and Summary 1

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