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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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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

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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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

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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Deep Learning: Long ShortTerm Memory Networks (LSTMs)

This video is a part of an online course that provides a comprehensive introduction to practial machine learning methods using MATLAB. In addition to short engaging videos, the course contains interactive, inbrowser MATLAB projects.

Complete course is available here: http://bit.ly/2Djmuc3
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See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.

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:
FeiFei Li: http://vision.stanford.edu/feifeili/
Justin Johnson: http://cs.stanford.edu/people/jcjohns/
Serena Yeung: http://ai.stanford.edu/~syyeung/

Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and selfdriving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these stateoftheart visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning endtoend models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cuttingedge research in computer vision.

Website:
http://cs231n.stanford.edu/

For additional learning opportunities please visit:
http://online.stanford.edu/

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!!

6.0. RNN & LSTM

딥러닝의 양대 축이라고 하면 Convolutional Neural Network (CNN)와 Recurrent Neural Network (RNN)를 꼽을 수 있을텐데요, 많은 분들이 RNN을 쓸 때 주로 이용되는 Long Shortterm Memory (LSTM)의 이해에 어려움을 겪으시는 것 같습니다.

그래서 준비했습니다. RNN과 LSTM 이해를 위한 완벽가이드! 금쪽같은 블로그인 Colah의 블로그 내용을 기반으로 RNN과 LSTM의 내부 메커니즘과 수식을 설명해보았습니다. 그럼 즐겁게 들어주세요~

[Link] Colah의 블로그, http://colah.github.io/posts/201508UnderstandingLSTMs/

전체강의 재생목록: https://www.youtube.com/playlist?list=PL0oFI08O71gKEXITQ7OG2SCCXkrtid7Fq
페이스북 페이지: https://www.facebook.com/deeplearningtalk/

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:
https://github.com/musikalkemist/DeepLearningForAudioWithPython/tree/master/18%20LSTM%20networks%20explained%20easily/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

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

lstm.