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CSB NN slides 100%

Using a neural network on ”Coders Strike Back” By bleuj Introduction Using a neural network on ”Coders Strike Back” What we try to achieve here Machine learning problem Main parts of the work because why not Gathering data from a good bot By bleuj 16 June 2016 Reference bot Simulating things and creating data outside of CodinGame Building the neural network function Neural network architecture Learning a neural network function Importing the neural network on CodinGame Importing the neural network on CodinGame Checking the result Contents Introduction What we try to achieve here Machine learning problem Main parts of the work Gathering data from a good bot Reference bot Simulating things and creating data outside of CodinGame Building the neural network function Neural network architecture Learning a neural network function Importing the neural network on CodinGame Importing the neural network on CodinGame Checking the result Using a neural network on ”Coders Strike Back” By bleuj Introduction What we try to achieve here Machine learning problem Main parts of the work Gathering data from a good bot Reference bot Simulating things and creating data outside of CodinGame Building the neural network function Neural network architecture Learning a neural network function Importing the neural network on CodinGame Importing the neural network on CodinGame Checking the result Contents Introduction What we try to achieve here Machine learning problem Main parts of the work Gathering data from a good bot Reference bot Simulating things and creating data outside of CodinGame Building the neural network function Neural network architecture Learning a neural network function Importing the neural network on CodinGame Importing the neural network on CodinGame Checking the result Using a neural network on ”Coders Strike Back” By bleuj Introduction What we try to achieve here Machine learning problem Main parts of the work Gathering data from a good bot Reference bot Simulating things and creating data outside of CodinGame Building the neural network function Neural network architecture Learning a neural network function Importing the neural network on CodinGame Importing the neural network on CodinGame Checking the result What we try to achieve here A light decent runner..

https://www.pdf-archive.com/2016/06/16/csb-nn-slides/

16/06/2016 www.pdf-archive.com

Neural Networks Software Market 98%

Executive Summary (1/2) Knowledge Based Value (KBV) Research Global Neural Networks Software Market Full Report:

https://www.pdf-archive.com/2018/03/05/neural-networks-software-market/

05/03/2018 www.pdf-archive.com

Neural Network Market 97%

Executive Summary (1/2) Knowledge Based Value (KBV) Research Global Neural Network Market Full Report:

https://www.pdf-archive.com/2017/08/29/neural-network-market/

29/08/2017 www.pdf-archive.com

Basics of Neural Nets 97%

l WORKSHOP B Y L O U I S E F R A N C I S The Basics of Neural Networks Demystified RTIFICIAL NEURAL NETWORKS A are the intriguing new high-tech tool for mining hidden gems in data.

https://www.pdf-archive.com/2018/03/23/basics-of-neural-nets/

23/03/2018 www.pdf-archive.com

IJEAS0405001 95%

2394-3661, Volume-4, Issue-5, May 2017 Artificial Neural Networks Usha Kumari, Dr.

https://www.pdf-archive.com/2017/09/10/ijeas0405001/

10/09/2017 www.pdf-archive.com

Forex robot TFOT algorithm 94%

Forex robot TFOT – algorithm explanation  Forex robot TFOT contains several modules:  • • • • Scalping strategy  Breakdown strategy  BackPropagation Neural Network  Forex news filter    ForexFactory  Calendar    News Module  Scalping Strategy    Neural  Network    Breakdown strategy      Trading signal can be generated by scalping strategy or (and) breakdown strategy. If signal generate scalping strategy,  robot will check all important news for EUR and USD currencies, and if in the near future there are no important news,  robot will open order. Robot will open order with full lot size ,if neural network passes a signal and with 50% (adjustable)  of lot size if neural network did not pass a signal. For this strategy we use trailing stop loss based ATR indicator.   Same algorithm for breakdown strategy, but with one exception: we do not use news filter for this strategy.   News Module  News module based on FF news indicator. Indicator loads news calendar from forexfactory website. News module has a  lot of adjustable parameters:     • • • • • • • • SymbolsFilter.On = true; Filter by simbol  SymbolsFilter = "EUR,USD"; Symbols   ShowOnlyCurrDay = false; Show calendar only for current day   IncludeHigh = true; show high impact news   IncludeMedium = true; show medium impact news   IncludeLow = false; show low impact news   IgnoreFilter.On = true; Ignore: holiday, speaks, tentative news  Etc.  But we have adjusted this module and sell robot with best settings.     Neural Network  Neural network based on Back Propagation Neural Network. You can read more about Back Propagation Neural Network  here: http://en.wikipedia.org/wiki/Backpropagation  In simple words, the neural network module  analyzes back test and for each order several bars before winning or losing  order, and stores patterns. In real trading module compare new patterns with stored patterns and makes decision.  Real Account Test  We test Forex Robot TFOT since 2010 and received excellent results.    You can check test here: http://www.myfxbook.com/members/iticsoftware/expert‐advisor‐tfot/65539  Forex Robot TFOT home page: http://iticsoftware.com/tfot6/   

https://www.pdf-archive.com/2013/04/08/forex-robot-tfot-algorithm/

08/04/2013 www.pdf-archive.com

IJETR2178 93%

Also in the past few years Artificial Neural Network becomes popular in the field of image compression.

https://www.pdf-archive.com/2017/09/09/ijetr2178/

09/09/2017 www.pdf-archive.com

ResearchPaper 92%

Learning Ticket Similarity with Context-sensitive Deep Neural Networks Iheb Ben Abdallah Durga Prasad Muni, Suman Roy, Yeung Tack Yan John John Lew Chiang, Navin Budhiraja Computer Science and Electrical Engineering, Ecole CentraleSupelec Grande Voie des Vignes, Chˆatenay-Malabry Paris, France 92290 Iheb.Benabdallah@supelec.fr Infosys Limited #44 Electronic City, Hosur Road Bangalore, India 560100 {DurgaPrasad Muni,Suman Roy,Yeung Chiang,Navin.

https://www.pdf-archive.com/2017/10/11/researchpaper/

11/10/2017 www.pdf-archive.com

OEM Summer2012Poster2 92%

Krešimir Josi , University of Houston Introduction Optimization problems in neural network models frequently appear in the theoretical neuroscience literature for a variety of purposes, such as putting upper bounds on network performance1, formulating general principles of network organization3,6, and even predicting how encoding strategies vary with changing stimulus statistics1.

https://www.pdf-archive.com/2015/11/25/oem-summer2012poster2/

25/11/2015 www.pdf-archive.com

22I16-IJAET0916945 v6 iss4 1639to1646 91%

22311963 ADAPTIVE GENETIC ALGORITHM WITH NEURAL NETWORK FOR MACHINERY FAULT DETECTION B.Kishore1, M.R.S.Satyanarayana2 and K.Sujatha3 1Assistant Professor, Dept.

https://www.pdf-archive.com/2014/07/04/22i16-ijaet0916945-v6-iss4-1639to1646/

04/07/2014 www.pdf-archive.com

26I17-IJAET1117400 v6 iss5 2187-2195 90%

The lightweight IDS has been developed by using a wrapper based feature selection algorithm that maximizes the specificity and sensitivity of the IDS as well as by employing a neural ensemble decision tree iterative procedure to evolve optimal features.

https://www.pdf-archive.com/2014/07/04/26i17-ijaet1117400-v6-iss5-2187-2195/

04/07/2014 www.pdf-archive.com

2168-4172-1-PB 90%

Computer Engineering and Intelligent Systems www.iiste.org ISSN 2222-1719 (Paper) ISSN 2222-2863 (Online) Vol 3, No.7, 2012 Implementation of a Modified Counterpropagation Neural Network Model in Online Handwritten Character Recognition System *Fenwa O.D., Emuoyibofarhe J.

https://www.pdf-archive.com/2013/12/30/2168-4172-1-pb/

30/12/2013 www.pdf-archive.com

US6584357 90%

“Tempo ACOUSTIC SIGNAL FROM NEURAL TIMING DIFFERENCE DATA (*) US 6,584,357 B1 607/54 Bernstein ........

https://www.pdf-archive.com/2017/06/16/us6584357/

16/06/2017 www.pdf-archive.com

11N13-IJAET0313450 revised 89%

2231-1963 ARTIFICIAL NEURAL NETWORK APPROACH FOR MODELING OF ADSORPTION OF NI (II) AND CR (VI) IONS SIMULTANEOUSLY PRESENT IN AQUEOUS SOLUTION USING ADSORBENT SYNTHESIZED FROM AEGEL MARMELOS FRUIT SHELL AND SYZYGIUM CUMINI SEED S.

https://www.pdf-archive.com/2013/05/13/11n13-ijaet0313450-revised/

13/05/2013 www.pdf-archive.com

25I14-IJAET0514200 v6 iss2 780to788 89%

2231-1963 PATH PLANNING OF MOBILE ROBOT USING REINFORCEMENT BASED ARTIFICIAL NEURAL NETWORK A.

https://www.pdf-archive.com/2013/05/13/25i14-ijaet0514200-v6-iss2-780to788/

13/05/2013 www.pdf-archive.com

北大CV April 15, 2007 89%

Estimation of Neural Network Models Dissertation Advisor:

https://www.pdf-archive.com/2018/04/24/cv-april-15-2007/

24/04/2018 www.pdf-archive.com

DeWall(3) 89%

Behavioral and Neural Evidence C.

https://www.pdf-archive.com/2016/09/11/dewall-3/

11/09/2016 www.pdf-archive.com

Deep Photo Style Transfer 89%

The Neural Style algorithm [5] (c) successfully transfers colors, but also introduces distortions that make the output look like a painting, which is undesirable in the context of photo style transfer.

https://www.pdf-archive.com/2017/03/31/deep-photo-style-transfer/

31/03/2017 www.pdf-archive.com

9I17-IJAET1117384 v6 iss5 2021-2032 89%

22311963 ESTIMATION OF PRESSURE DROP FOR FLOW OF CMC IN AQUEOUS SOLUTION USING ARTIFICIAL NEURAL NETWORK Shekhar Pandharipande1, Rachana S.

https://www.pdf-archive.com/2014/07/04/9i17-ijaet1117384-v6-iss5-2021-2032/

04/07/2014 www.pdf-archive.com

15N19-IJAET0319399 v7 iss1 127-134 89%

22311963 SIMULTANEOUS LEAK DETECTION WITH MAGNITUDE AT TWO POSITIONS FOR FLOW OF WATER IN PIPELINE USING ARTIFICIAL NEURAL NETWORK Shekhar Pandharipande, Prashant Rai and Siddharth Sharma Department of Chemical Engineering, Laxminarayan Institute of Technology, RTM Nagpur University, Nagpur, India ABSTRACT Chemical process industries have complex structures comprising of equipments &

https://www.pdf-archive.com/2014/07/04/15n19-ijaet0319399-v7-iss1-127-134/

04/07/2014 www.pdf-archive.com

fake-news-detection-final 88%

A convolutional neural network (CNN) is used by [Wang 2017] for capturing deceptive styles in the fake news.

https://www.pdf-archive.com/2018/01/06/fake-news-detection-final/

06/01/2018 www.pdf-archive.com

Artificial neural networks in petroleum engineering 88%

Petroleum University of Technology Abadan Faculty of Petroleum Engineering (Shahid Tondgooyan) Department of Petroleum Exploration Engineering Prediction of Crude Oils PVT Properties Using Artificial Neural Networks Master of Science Thesis By:

https://www.pdf-archive.com/2015/07/28/artificial-neural-networks-in-petroleum-engineering/

28/07/2015 www.pdf-archive.com

IJETR2275 88%

It describe the developed and dynamic method of designing finite impulse response filter with automatic rapid and less error by an efficient genetic and neural approach.

https://www.pdf-archive.com/2017/09/09/ijetr2275/

09/09/2017 www.pdf-archive.com

IJETR2192 88%

The main focus of the paper is to describe the developed and dynamic method of designing finite impulse response filter with automatic rapid and less error by an efficient genetic and neural approach.

https://www.pdf-archive.com/2017/09/09/ijetr2192/

09/09/2017 www.pdf-archive.com

4N13-IJAET0313474 revised 88%

2231-1963 OPTIMUM LEARNING RATE FOR CLASSIFICATION PROBLEM WITH MLP IN DATA MINING Lalitha Saroja Thota1 and Suresh Babu Changalasetty2 1 2 Department of Computer Science, King Khalid University, Abha, KSA Department of Computer Engineering, King Khalid University, Abha, KSA ABSTRACT Neural networks have been used effectively in a number of applications including remote sensing, expert systems, image processing, data mining, decision systems etc.

https://www.pdf-archive.com/2013/05/13/4n13-ijaet0313474-revised/

13/05/2013 www.pdf-archive.com