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Simple Explanation of LSTM | Deep Learning Tutorial 36 (Tensorflow, Keras & Python)


LSTM or long short term memory is a special type of RNN that solves traditional RNN’s short term memory problem. In this video I will give a very simple explanation of LSTM using some real life examples so that you can understand this difficult topic easily. Also refer to following blogs to explore math and understand few more details.

http://colah.github.io/posts/201508UnderstandingLSTMs/

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Simple Explanation of LSTM | Deep Learning Tutorial 36 (Tensorflow, Keras & Python)

Illustrated Guide to LSTM&39;s and GRU&39;s: A step by step explanation


LSTM’s and GRU’s are widely used in state of the art deep learning models. For those just getting into machine learning and deep learning, this is a guide in plain English with helpful visuals to help you grok LSTM’s and GRU’s.

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Sources
http://www.wildml.com/2015/10/recurrentneuralnetworktutorialpart4implementingagrulstmrnnwithpythonandtheano/
http://colah.github.io/posts/201508UnderstandingLSTMs/
https://www.youtube.com/watch?v=WCUNPb5EYI

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Illustrated Guide to LSTM&39;s and GRU&39;s: A step by step explanation

Recurrent Neural Networks (RNN) and Long ShortTerm Memory (LSTM)


Part of the EndtoEnd Machine Learning School Course 193, How Neural Networks Work at https://e2eml.school/193

Recurrent Neural Networks (RNN) and Long ShortTerm Memory (LSTM)

LSTM Networks EXPLAINED!


Recurrent neural nets are very versatile. However, they don’t work well for longer sequences. Why is this the case? You’ll understand that now. And we delve into one of the most common Recurrent Neural Network Architectures : LSTM. We also build a text generator in Keras to generate state union speeches.

Code for this video: https://github.com/ajhalthor/Keras_LSTM_Text_Generator

REFERENCES
[1] LSTM Landmark paper (Sepp Hochreiter ): https://www.bioinf.jku.at/publications/older/2604.pdf
[1] Slides from the Deep Learning book for RNNs: https://www.deeplearningbook.org/slides/10_rnn.pdf
[2] Andrej Karpathy’s Blog + Code (You can probably understand more from this now!): http://karpathy.github.io/2015/05/21/rnneffectiveness/
[3] The Deep learning Book on Sequence Modeling: https://www.deeplearningbook.org/contents/rnn.html
[4] Colah’s blog on LSTMs: http://colah.github.io/posts/201508UnderstandingLSTMs/
[6] Visualizing and Understanding RNNs : https://arxiv.org/pdf/1506.02078.pdf

LSTM Networks  EXPLAINED!

Xây dựng mạng SimpleRNN, LSTM, Bidirectional


Xây dựng 3 model: SimpleRNN, LSTM (Long shortterm memories), Bidirectional cho bài toán nhận diện nhị phân.

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Xây dựng mạng SimpleRNN, LSTM, Bidirectional

Deep Learning: Long ShortTerm Memory Networks (LSTMs)


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Deep Learning: Long ShortTerm Memory Networks (LSTMs)

Tutorial 34 LSTM Recurrent Neural Network In Depth Intuition


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Reference Link: https://colah.github.io/posts/201508UnderstandingLSTMs/

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Tutorial 34 LSTM Recurrent Neural Network In Depth Intuition

Lecture 10 | Recurrent Neural Networks


In Lecture 10 we discuss the use of recurrent neural networks for modeling sequence data. We show how recurrent neural networks can be used for language modeling and image captioning, and how soft spatial attention can be incorporated into image captioning models. We discuss different architectures for recurrent neural networks, including Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU).

Keywords: Recurrent neural networks, RNN, language modeling, image captioning, soft attention, LSTM, GRU

Slides: http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture10.pdf

Convolutional Neural Networks for Visual Recognition

Instructors:
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Lecture 10 | Recurrent Neural Networks

AI로 삼성전자의 주가를 예측해보자 (with LSTM)


안녕하세요. 흑우스토리입니다.
오늘 시간에는 AI자동매매 3번째 시간으로 실제 분석결과를 보여드리려합니다.
정말 다양한 데이터를 보여드리려했지만 너무 과도한것 같아 간략하게 삼성전자로 만들어보았습니다.
AI자동매매에 관심있는 구독자님들에게 도움이 되었으면 좋겠습니다.
성투하세요!!

AI로 삼성전자의 주가를 예측해보자 (with LSTM)

18 Long Short Term Memory (LSTM) Networks Explained Easily


In this video, you’ll learn how Long Short Term Memory (LSTM) networks work. We’ll take a look at LSTM cells both architecturally and mathematically, and compare them against simple RNN cells.

Video slides:
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Check out the articles below to learn more about LSTMs and GRUs:

“Understanding LSTM Networks”
http://colah.github.io/posts/201508UnderstandingLSTMs/

“Understanding GRU Networks”
https://towardsdatascience.com/understandinggrunetworks2ef37df6c9be

18 Long Short Term Memory (LSTM) Networks Explained Easily

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Illustrated Guide to LSTM's and GRU's: A step by step explanation

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Illustrated Guide to LSTM's and GRU's: A step by step explanation

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33 thoughts on “Illustrated Guide to LSTM's and GRU's: A step by step explanation | lstm | 提供最新和弦的网站”

  1. Should Cell state and Hidden state have same size ? What about the concatenation of Ht-1 and Xt, it should increase the size of the vector that goes into the first sigmoid and create a problem for matrix multiplication with Ct-1 right ?

  2. I tried to understand this since 2016, gave up so many times. Now finally I understood. Thank you so much! Can't thank you enough!

  3. This was very helpful! I am trying to figure out how to transform an input which is actually ment for a lstm cell into an input that can be used by the gru cell. The reason i am trying to do this is that i want to combine lstm an gru cells in different layers of a model architecture

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