511 lines
14 KiB
BibTeX
511 lines
14 KiB
BibTeX
@inproceedings{SaTdpsmm,
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title={Sentiment analysis of Twitter data for predicting stock market movements},
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author={Pagolu, Venkata Sasank and Reddy, Kamal Nayan and Panda, Ganapati and Majhi, Babita},
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booktitle={2016 international conference on signal processing, communication, power and embedded system (SCOPES)},
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pages={1345--1350},
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year={2016},
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organization={IEEE},
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url = {https://arxiv.org/pdf/1610.09225.pdf}
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}
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@inproceedings{nlAeiBTCPSO,
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title={Non-linear autoregressive with exogeneous input (NARX) Bitcoin price prediction model using PSO-optimized parameters and moving average technical indicators},
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author={Indera, NI and Yassin, IM and Zabidi, A and Rizman, ZI},
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booktitle={Journal of Fundamental and Applied Sciences. Vol.35, No.35},
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pages={791--808},
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year={2017},
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organization={University of El Oued},
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url = {https://www.ajol.info/index.php/jfas/article/viewFile/165614/155073}
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}
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@inproceedings{ISO9000,
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title={Quality management principles:ISO9000-IS9001},
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author={ISO},
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booktitle={},
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pages={},
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year={2015},
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organization={ISO},
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url={https://www.iso.org/files/live/sites/isoorg/files/archive/pdf/en/pub100080.pdf}
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}
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@inproceedings{BTCFTsent,
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title={Predicting Bitcoin price fluctuation with Twitter sentiment analysis},
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author={Evita Stenqvist, Jacob Lonno},
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booktitle={},
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pages={},
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year={2017},
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organization={Diva},
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url = {http://www.diva-portal.org/smash/get/diva2:1110776/FULLTEXT01.pdf}
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}
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@inproceedings{BTCRNN,
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title={Predict Tomorrows Bitcoin (BTC) Price with Recurrent Neural Networks},
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author={Orhan Gazi Yalcin},
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booktitle={},
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pages={},
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year={2018},
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organization={Towards Data Science},
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url = {https://towardsdatascience.com/using-recurrent-neural-networks-to-predict-bitcoin-btc-prices-c4ff70f9f3e4}
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}
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@inproceedings{StPNSentA,
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title={Stock Predictions through News Sentiment Analysis},
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author={Intel-Corporation},
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booktitle={},
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pages={},
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year={2017},
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organization={Code Project},
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url = {https://www.codeproject.com/Articles/1201444/Stock-Predictions-through-News-Sentiment-Analysis}
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}
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@inproceedings{MLBTCpred,
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title={Predicting the Price of Bitcoin Using Machine Learning},
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author={Sean McNally, Jason Roche, Simon Caton},
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booktitle={2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP)},
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pages={344--347},
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year={2018},
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organization={IEEE},
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url = {https://ieeexplore.ieee.org/abstract/document/8374483}
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}
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@inproceedings{SearchTweets,
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title={Search Tweets},
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author={Twitter},
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booktitle={},
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pages={},
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year={2018},
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organization={Twitter Developers},
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url = {https://developer.twitter.com/en/docs/tweets/search/overview}
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}
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@inproceedings{ConStream,
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title={Consuming streaming data},
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author={Twitter},
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booktitle={},
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pages={},
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year={2018},
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organization={Twitter Developers},
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url = {https://developer.twitter.com/en/docs/tutorials/consuming-streaming-data.html}
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}
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@inproceedings{TweepyStream,
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title={Streaming With Tweepy},
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author={Joshua Roesslein},
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booktitle={},
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pages={},
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year={2009},
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organization={Tweepy},
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url = {http://docs.tweepy.org/en/v3.4.0/streaming_how_to.html}
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}
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@inproceedings{PolClassPatients,
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title={Using Linked Data for polarity classification of patients experiences},
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author={Mehrnoush Shamsfard, Samira Noferesti},
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booktitle={Journal of Biomedical Informatics},
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pages={6-19},
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year={2015},
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organization={Elsevier},
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url = {https://www.sciencedirect.com/science/article/pii/S1532046415001276}
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}
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@inproceedings{LexiconSocSent,
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title={Social media sentiment analysis: lexicon versus machine learning},
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author={Chedia Dhaoui, Cynthia M. Webster, Lay Peng Tan},
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booktitle={Journal of Consumer Marketing, Volume 34. Issue 6},
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pages={},
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year={2017},
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organization={Emerald Insight},
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url = {https://www.emeraldinsight.com/doi/pdfplus/10.1108/JCM-03-2017-2141}
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}
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@inproceedings{VADERPaper,
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title={VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text},
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author={C.J. Hutto and Eric Gilbert},
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booktitle={Eighth International Conference on Weblogs and Social Media (ICWSM-14)},
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pages={},
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year={2014},
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organization={Ann Arbor, MI},
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url = {https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/download/8109/8122}
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}
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@inproceedings{WisCrowds,
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title={Wisdom of Crowds},
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author={Will Kenton},
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booktitle={},
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pages={},
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year={2018},
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organization={Investopedia},
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url = {https://www.investopedia.com/terms/w/wisdom-crowds.asp}
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}
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@inproceedings{NNDLBegin,
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title={A Beginner's Guide to Neural Networks and Deep Learning},
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author={Skymind},
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booktitle={A.I. Wiki},
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pages={},
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year={2018},
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organization={Skymind},
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url = {https://skymind.ai/wiki/neural-network}
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}
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@inproceedings{WhatNN,
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title={What is a neural network},
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author={Jonas DeMuro},
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booktitle={World of tech},
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pages={},
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year={2018},
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organization={techradar},
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url = {https://www.techradar.com/uk/news/what-is-a-neural-network}
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}
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@inproceedings{SupdictL,
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title={Supervised dictionary learning},
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author={Mairal, J., Ponce, J., Sapiro, G., Zisserman, A. and Bach, F.R., },
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booktitle={Advances in neural information processing systems },
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pages={1033--1040},
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year={2009},
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organization={NIPS Proceedings},
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url = {http://papers.nips.cc/paper/3448-supervised-dictionary-learning}
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}
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@inproceedings{ErrorProp,
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title={Learning internal representations by error propagation},
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author={Rumelhart, David E and Hinton, Geoffrey E and Williams, Ronald J},
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booktitle={},
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pages={},
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year={1985},
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organization={California Univ San Diego La Jolla Inst for Cognitive Science},
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url = {https://apps.dtic.mil/docs/citations/ADA164453}
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}
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@inproceedings{BeginLSTMRNN,
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title={A Beginner's Guide to LSTMs and Recurrent Neural Networks},
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author={Skymind},
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booktitle={A.I. Wiki},
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pages={},
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year={2018},
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organization={Skymind},
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url = {https://skymind.ai/wiki/lstm}
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}
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@inproceedings{RNNLSTMtds,
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title={Recurrent Neural Networks and LSTM},
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author={Niklas Donges},
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booktitle={},
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pages={},
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year={2018},
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organization={Towards Data Science},
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url = {https://towardsdatascience.com/recurrent-neural-networks-and-lstm-4b601dd822a5}
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}
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@inproceedings{NNEgrad,
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title={A Gentle Introduction to Exploding Gradients in Neural Networks},
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author={Jason Brownlee, PhD.},
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booktitle={},
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pages={},
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year={2017},
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organization={Machine Larning Mastery},
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url = {https://machinelearningmastery.com/exploding-gradients-in-neural-networks/}
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}
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@inproceedings{RNNvanishGrad,
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title={Recurrent Neural Networks (RNN) - The Vanishing Gradient Problem},
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author={Super Data Science Team},
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booktitle={},
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pages={},
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year={2018},
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organization={Super Data Science},
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url = {https://www.superdatascience.com/blogs/recurrent-neural-networks-rnn-the-vanishing-gradient-problem}
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}
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@inproceedings{LSTM,
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title={Long short-term memory},
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author={Hochreiter, Sepp and Schmidhuber, Jurgen},
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booktitle={Neural computation, Volume 9. 8},
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pages={1735--1780},
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year={1997},
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organization={MIT Press},
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url = {https://www.bioinf.jku.at/publications/older/2604.pdf}
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}
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@inproceedings{LSTMdia,
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title={Understanding LSTM and its diagrams},
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author={Shi Yan},
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booktitle={},
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pages={},
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year={Mar 13, 2016},
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organization={Medium},
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url = {https://medium.com/mlreview/understanding-lstm-and-its-diagrams-37e2f46f1714}
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}
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@inproceedings{LSTMmaths,
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title={Understanding LSTM Networks},
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author={Christopher Olah},
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booktitle={},
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pages={},
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year={2015},
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organization={},
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url = {https://colah.github.io/posts/2015-08-Understanding-LSTMs}
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}
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@inproceedings{LSTMforetime,
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title={Using LSTMs to forecast time-series},
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author={Ravindra Kompella},
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booktitle={},
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pages={},
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year={2018},
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organization={Towards Data Science},
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url = {https://towardsdatascience.com/using-lstms-to-forecast-time-series-4ab688386b1f}
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}
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@inproceedings{TensorFlow,
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title={Tensorflow: A system for large-scale machine learning},
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author={Abadi, Martin and Barham, Paul and Chen, Jianmin and Chen, Zhifeng and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Irving, Geoffrey and Isard, Michael and others},
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booktitle={12th Symposium on Operating Systems Design and Implementation 16)},
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pages={265--283},
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year={2016},
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url={https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf}
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}
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@inproceedings{OptSGD,
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title={Optimization: Stochastic Gradient Descent},
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author={Stanford},
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booktitle={UFLDL Tutorial},
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pages={},
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year={},
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url={http://deeplearning.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent}
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}
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@inproceedings{Optimisers,
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title={What are differences between update rules like AdaDelta, RMSProp, AdaGrad, and Adam},
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author={Rajarshee Mitra},
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booktitle={},
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pages={},
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year={2016},
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organization={Quora},
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url={https://www.quora.com/What-are-differences-between-update-rules-like-AdaDelta-RMSProp-AdaGrad-and-AdaM}
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}
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@inproceedings{OptVariants,
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title={Variants of rmsprop and adagrad with logarithmic regret bounds},
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author={Mukkamala, Mahesh Chandra and Hein, Matthias},
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booktitle={Proceedings of the 34th International Conference on Machine Learning-Volume 70},
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pages={2545--2553},
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year={2017},
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organization={JMLR.org},
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url={https://arxiv.org/pdf/1706.05507.pdf}
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}
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@inproceedings{OverOpt,
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title={Overview of different Optimizers for neural networks},
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author={Renu Khandelwal},
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booktitle={},
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pages={},
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year={2019},
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organization={Medium},
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url={https://medium.com/datadriveninvestor/overview-of-different-optimizers-for-neural-networks-e0ed119440c3}
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}
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@inproceedings{Adam,
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title={Adam: A method for Stochastic Optimization},
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author={Diederik P. Kingma, Jimmy Lei Ba},
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booktitle={arXiv preprint arXiv:1412.6980},
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pages={},
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year={2014},
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organization={arXiv},
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url={https://arxiv.org/pdf/1412.6980.pdf}
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}
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@inproceedings{StudyNBC,
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title={An empirical study of the naive Bayes classifier},
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author={Rish, Irina and others},
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booktitle={IJCAI 2001 workshop on empirical methods in artificial intelligence},
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volume={3},
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number={22},
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pages={41--46},
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year={2001},
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url={https://www.cc.gatech.edu/~isbell/reading/papers/Rish.pdf}
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}
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|
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@inproceedings{TFIDFBOW,
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title={A Beginner's Guide to Bag of Words and TF-IDF},
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author={Skymind},
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booktitle={A.I Wiki},
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pages={},
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year={2018},
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organization={Skymind},
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url={https://skymind.ai/wiki/bagofwords-tf-idf}
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}
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|
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|
@inproceedings{SpamCScratch,
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title={Spam Classifier in Python from scratch},
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author={Tejan Karmali},
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booktitle={},
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pages={},
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year={Aug 2, 2017},
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organization={Towards Data Science},
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url={https://towardsdatascience.com/spam-classifier-in-python-from-scratch-27a98ddd8e73}
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}
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|
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|
@inproceedings{TweepyDoc,
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title={Tweepy Documentation},
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author={Joshua Roesslein},
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booktitle={},
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volume={},
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number={},
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pages={},
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year={2009},
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url={http://docs.tweepy.org/en/v3.5.0/}
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}
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@inproceedings{TFvsThe,
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title={Tensorflow Vs. Theano: What Do Researchers Prefer As An Artificial Intelligence Framework},
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author={Srishti Deoras},
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booktitle={},
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volume={},
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number={},
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pages={},
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year={2017},
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organization={Analytics India},
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url={https://www.analyticsindiamag.com/tensorflow-vs-theano-researchers-prefer-artificial-intelligence-framework}
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}
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@inproceedings{btcCharts,
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title={},
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author={bitcoincharts},
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booktitle={},
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pages={},
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year={},
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organization={Bitcoin Charts},
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url={http://api.bitcoincharts.com/v1/csv/}
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}
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@inproceedings{langdectNLTK,
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title={Detecting Text Language With Python and NLTK},
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author={Alejandro Nolla},
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booktitle={},
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pages={},
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year={},
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organization={Alejandro Nolla Blog},
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url={http://blog.alejandronolla.com/2013/05/15/detecting-text-language-with-python-and-nltk/}
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}
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@inproceedings{LanNgram,
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title={A tutorial on Automatic Language Identification - ngram based},
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author={Practical Cryptography},
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booktitle={},
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pages={},
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|
year={},
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|
organization={Practical Cryptography},
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|
url={http://practicalcryptography.com/miscellaneous/machine-learning/tutorial-automatic-language-identification-ngram-b/}
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|
}
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|
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|
@inproceedings{StemvsLem,
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title={What is the difference between stemming and lemmatization},
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|
author={Tita Risueno},
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|
booktitle={},
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|
pages={},
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|
year={Feb 26, 2018},
|
|
organization={Bitext},
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|
url={https://blog.bitext.com/what-is-the-difference-between-stemming-and-lemmatization/}
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|
}
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|
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@inproceedings{RegularisationSc,
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title={Neural Network Bias: Bias Neuron, Overfitting and Underfitting},
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author={Missing Link AI},
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booktitle={},
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|
pages={},
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|
year={},
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organization={Missing Link AI},
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|
url={https://missinglink.ai/guides/neural-network-concepts/neural-network-bias-bias-neuron-overfitting-underfitting/}
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|
}
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|
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@inproceedings{dropoutM,
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title={Dropout in (Deep) Machine learning},
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author={Amar Budhiraja},
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booktitle={},
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|
pages={},
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|
year={Dec 15, 2016},
|
|
organization={Medium},
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|
url={https://medium.com/@amarbudhiraja/https-medium-com-amarbudhiraja-learning-less-to-learn-better-dropout-in-deep-machine-learning-74334da4bfc5}
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}
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|
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@inproceedings{dropoutKeras,
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title={Dropout},
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author={Keras Team},
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booktitle={},
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pages={},
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year={},
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organization={Keras},
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url={https://keras.io/layers/core/#dropout}
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}
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|
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@inproceedings{NValgor,
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title={Naive Bayes},
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author={scikit-learn developers},
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booktitle={},
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pages={},
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|
year={},
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organization={Scikit-Learn},
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url={https://scikit-learn.org/stable/modules/naive_bayes.html}
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|
}
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|
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@inproceedings{SpamOrHamGit,
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title={Spam-or-Ham},
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author={Tejan Karmali - tejank10},
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booktitle={},
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|
pages={},
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|
year={Aug 2, 2017},
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organization={Github},
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url={https://github.com/tejank10/Spam-or-Ham}
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}
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|
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@inproceedings{MAPE,
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title={Mean absolute percentage error (MAPE)},
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author={Stephanie},
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booktitle={},
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|
pages={},
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|
year={Sep 8, 2017},
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|
organization={Statistics HowTo},
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url={https://www.statisticshowto.datasciencecentral.com/mean-absolute-percentage-error-mape/}
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}
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@inproceedings{RMSEMAE,
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title={Choosing the Right Metric for Evaluating Machine Learning Models},
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author={Alvira Swalin},
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|
booktitle={},
|
|
pages={},
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|
year={Apr 7, 2018},
|
|
organization={Medium},
|
|
url={https://medium.com/usf-msds/choosing-the-right-metric-for-machine-learning-models-part-1-a99d7d7414e4}
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|
}
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@inproceedings{MSE,
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title={Machine learning: an introduction to mean squared error and regression lines},
|
|
author={Moshe Binieli},
|
|
booktitle={},
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|
pages={},
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|
year={Oct 16, 2018},
|
|
organization={Medium},
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|
url={https://medium.freecodecamp.org/machine-learning-mean-squared-error-regression-line-c7dde9a26b93}
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|
}
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|
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@inproceedings{MBE,
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title={MAE and RMSE Which Metric is Better},
|
|
author={JJ},
|
|
booktitle={},
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|
pages={},
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year={Mar 23, 2016},
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organization={Medium},
|
|
url={https://medium.com/human-in-a-machine-world/mae-and-rmse-which-metric-is-better-e60ac3bde13d}
|
|
}
|
|
|
|
@inproceedings{TwitterTerms,
|
|
title={Developer Agreement and Policy},
|
|
author={Twitter},
|
|
booktitle={},
|
|
pages={},
|
|
year={Effective: May 25, 2018},
|
|
organization={Twitter Corp.},
|
|
url={https://developer.twitter.com/en/developer-terms/agreement-and-policy.html}
|
|
} |