論文まとめ：Gated Recurrent Convolutional Neural Network for OCR - Qiita
はじめに NIPS2017から、J. Wang らの Gated Recurrent Convolutional Neural Network for OCR をまとめてみた。 NIPS2017の論文ページはこちら。 http://papers.nips.cc/paper/6637-gated-recurrent-convolution-neural-network-for-ocr 著者らのコードはこちら。 https://github.com/Jianfeng1991/GRCNN-for-OCR 概要 OCRタスクのモデル RCNN(recurrent convolutional neural network)に gate を加えた GRCNN(Gated RCNN)を用いた この gate はRCL(recurrent convolution layer)における context mo
Training a Recurrent Neural Network to Compose Music | maraoz
Over the past weeks I’ve been slowly learning about recent developments in Machine Learning, specifically Neural Networks. I’ve seen really mind-blowing examples of the power of such architectures, from recreating images using particular art styles to automatically forming word representations that account for pretty high-level semantic relations. Recurrent Neural Networks (their most frecuent for
GitHub - robbiebarrat/rapping-neural-network: Rap song writing recurrent neural network trained on Kanye West's entire discography
Click to expand Bust a playa with the kids I never had All his time, all he had, all he had, all he had Most you rappers don't even stop to get the most press kit Playas is jealous cause we got the whole city lit But without it I'd be worried if they playing that bullshit You wanna complain about the nights even wilder I swear to God I hope you have got to hear I'll touch every curve of your favor
GitHub - gy910210/rnn-from-scratch: Implementing Recurrent Neural Network from Scratch
The architecture of RNN can be as the following figure. You can find that the parameters (W, U, V) are shared in different time steps. And the output in each time step can be softmax. So you can use cross entropy loss as an error function and use some optimizing method (e.g. gradient descent) to calculate the optimized parameters (W, U, V). Let recap the equations of our RNN: We also defined our l
Learn to Add Numbers with an Encoder-Decoder LSTM Recurrent Neural Network - MachineLearningMastery.com
Long Short-Term Memory (LSTM) networks are a type of Recurrent Neural Network (RNN) that are capable of learning the relationships between elements in an input sequence. A good demonstration of LSTMs is to learn how to combine multiple terms together using a mathematical operation like a sum and outputting the result of the calculation. A common mistake made by beginners is to simply learn the map
How to build a Recurrent Neural Network in TensorFlow (1/7)
Dear reader, This article has been republished at Educaora and has also been open sourced. Unfortunately TensorFlow 2.0 changed the API so it is broken for later versions. Any help to make the tutorials up to date are greatly appreciated. I also recommend you looking into PyTorch. In this tutorial I’ll explain how to build a simple working Recurrent Neural Network in TensorFlow. This is the first
Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
Recurrent neural networks (RNNs) have been widely used for processing sequential data. However, RNNs are commonly difficult to train due to the well-known gradient vanishing and exploding problems and hard to learn long-term patterns. Long short-term memory (LSTM) and gated recurrent unit (GRU) were developed to address these problems, but the use of hyperbolic tangent and the sigmoid action funct
TopicRNN: A Recurrent Neural Network with Long-Range Semantic Dependency
In this paper, we propose TopicRNN, a recurrent neural network (RNN)-based language model designed to directly capture the global semantic meaning relating words in a document via latent topics. Because of their sequential nature, RNNs are good at capturing the local structure of a word sequence - both semantic and syntactic - but might face difficulty remembering long-range dependencies. Intuitiv
Robsut Wrod Reocginiton via semi-Character Recurrent Neural Network
Language processing mechanism by humans is generally more robust than computers. The Cmabrigde Uinervtisy (Cambridge University) effect from the psycholinguistics literature has demonstrated such a robust word processing mechanism, where jumbled words (e.g. Cmabrigde / Cambridge) are recognized with little cost. On the other hand, computational models for word recognition (e.g. spelling checkers)
Build an AI Programmer using Recurrent Neural Network (1) – Program Creek
Recurrent Neural Networks (RNNs) are gaining a lot of attention in recent years because it has shown great promise in many natural language processing tasks. Despite their popularity, there are a limited number of tutorials which explain how to implement a simple and interesting application using the state-of-art tools. In this series, we will use a recurrent neural network to train an AI programm
MariFlow - Self-Driving Mario Kart w/Recurrent Neural Network
I trained a recurrent neural network to play Mario Kart human-style. MariFlow Manual & Download: https://docs.google.com/document/d/1p4ZOtziLmhf0jPbZTTaFxSKdYqE91dYcTNqTVdd6es4/edit?usp=sharing Mushroom Cup: https://www.twitch.tv/videos/183296063 Flower Cup: https://www.twitch.tv/videos/183296268 Star Cup: https://www.twitch.tv/videos/183296400 SethBling Twitter: http://twitter.com/sethbling Set
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
GitHub - made2591/go-perceptron-go: A single / multi layer / recurrent neural network written in Golang.
http://www.vision.cs.chubu.ac.jp/MPRG/F_group/F172_kamiya2016.pdf
GitHub - keisks/robsut-wrod-reocginiton: Robsut Wrod Reocginiton via semi-Character Recurrent Neural Network
https://udrc.eng.ed.ac.uk/sites/udrc.eng.ed.ac.uk/files/attachments/UDRC_RNN_LSTM_LibrariesTutorial.pdf
Reddit - Dive into anything
GitHub - salesforce/pytorch-qrnn: PyTorch implementation of the Quasi-Recurrent Neural Network - up to 16 times faster than NVIDIA's cuDNN LSTM
GitHub - tomepel/Technical_Book_DL: This note presents in a technical though hopefully pedagogical way the three most common forms of neural network architectures: Feedforward, Convolutional and Recurrent.
GitHub - minimaxir/textgenrnn: Easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code.
bioRxiv Neuroscience on Twitter: "Deep Recurrent Neural Network Reveals a Hierarchy of Process Memory during Dynamic Natural Vision https://t.co/vYASlRjUsv #biorxiv_neursci"
