#back propagation

[stephlee1231995-blog]

Python深度學習:BackPropagation

Photo by ThisIsEngineering on Pexels.com 繼上次了解了Gradient Descent之後，我們已經知道訓練模型其實就是在試圖找出能使損失函數極小化的最佳權重w以及最佳偏值b，而方式就是透過對損失函數進行偏微分。然而，在一個非常多層的神經網路中要直接計算損失函數對w和b的偏微分是很困難的，再加上人們發現其實神經網路的每一層的梯度計算都會互相關聯，因此發展出了反向傳播法這樣一個更有效率進行微分計算的方法。 反向傳播法? 反向傳播法簡而言之，就是從輸出層(output…

View On WordPress

[vinodavvari]

Deep Learning & Machine Learning

[bookpiofficial]

ANN with Fuzzification in Software Cost Estimation | Chapter 10 | Emerging Issues in Science and Technology Vol. 3

This paper tries to provide a prototype for cost estimation with fuzzification and ANN, which is a propositional calculus based on functional point of the training data set. These kind of solutions are regardless of mathematical views of the problem and it is useful for such case which does not have precise inputs, for instance in software cost estimation. There are methods in four major categories such as algorithmic COCOMO-II (Constructive Cost Model) model, functional point, analogy, expert judgment, top down and bottom down method which have their own pros and cons. Their merits will be discussed in comparison with the proposed methodology of ANN BP (Artificial Neural Network Back Propogation) and fuzzy logic; in order to achieve their more gain in estimation. It will lead us to know that which method should be used with specific conditions.

Author(s) Details

Dinesh Bhagwan Hanchate Department of Computer Engineering, VP’s KBIET, Baramati, Pune, India.

Mahsa Hassankashi Department of Computer Science and Engineering, Ovidius GmbH, Berlin, Germany.

View Books: http://bp.bookpi.org/index.php/bpi/catalog/book/144

[attorneyandlawyer]

NEURAL NETWORK FOR RECOGNITION OF HANDWRITTEN AND DIGITS BACK PROPAGATION

NEURAL NETWORK FOR RECOGNITION OF HANDWRITTEN AND DIGITS BACK PROPAGATION

NEURAL NETWORK FOR RECOGNITION OF HANDWRITTEN AND DIGITS BACK PROPAGATION

TABLE OF CONTENT Title page- – – – – – – – – i Approval page – – – – – – – -ii Dedication – – – – – – – – -iii Acknowledgement – – – – – – – -iv Abstract – – – – – – – – – -v Table of content – – – – – – – -vi

CHAPTER ONE INTRODUCTION – – – – – – – -1 1.0 Background of the study – – – – -1 1.1 Statement of the problem – – –…

View On WordPress

[ijtsrd]

Implementation and Performance Evaluation of Neural Network for English Alphabet Recognition System

by Myat Thida Tun" Implementation and Performance Evaluation of Neural Network for English Alphabet Recognition System" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, 

URL: http://www.ijtsrd.com/papers/ijtsrd15863.pdf 

Direct URL: http://www.ijtsrd.com/engineering/information-technology/15863/implementation-and-performance-evaluation-of-neural-network-for-english-alphabet-recognition-system/myat-thida-tun

call for paper technology, technology journal, indexed journal

One of the most classical applications of the Artificial Neural Network is the character recognition system. This system is the base for many different types of applications in various fields, many of which are used in daily lives. Cost effective and less time consuming, businesses, post offices, banks, security systems, and even the field of robotics employ this system as the base of their operations. For character recognition, there are many prosperous algorithms for training neural networks. Back propagation (BP) is the most popular algorithm for supervised training multilayer neural networks. In this thesis, Back propagation (BP) algorithm is implemented for the training of multilayer neural networks employing in character recognition system. The neural network architecture used in this implementation is a fully connected three layer network. The network can train over 16 characters since the 4-element output vector is used as output units. This thesis also evaluates the performance of Back propagation (BP) algorithm with various learning rates and mean square errors. MATLAB Programming language is used for implementation. 

[ijtsrd]

Alphabet Recognition System Based on Artifical Neural Network

by Myat Thida Tun | - | -" Alphabet Recognition System Based on Artifical Neural Network" 

Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, 

URL: http://www.ijtsrd.com/papers/ijtsrd15854.pdf 

Direct URL: http://www.ijtsrd.com/engineering/information-technology/15854/alphabet-recognition-system-based-on-artifical-neural-network/myat-thida-tun

call for paper technology, technology journal, indexed journal

One of the most classical applications of the Artificial Neural Network is the character recognition system. This system is the base for many different types of applications in various fields, many of which are used in daily lives. Cost effective and less time consuming, businesses, post offices, banks, security systems, and even the field of robotics employ this system as the base of their operations. For character recognition, there are many prosperous algorithms for training neural networks. Back propagation (BP) is the most popular algorithm for supervised training multilayer neural networks. In this thesis, Back propagation (BP) algorithm is implemented for the training of multilayer neural networks employing in character recognition system. The neural network architecture used in this implementation is a fully connected three layer network. The network can train over 16 characters since the 4-element output vector is used as output units. This thesis also evaluates the performance of Back propagation (BP) algorithm with various learning rates and mean square errors. MATLAB Programming language is used for implementation. 

[zzkt]

Why we should be Deeply Suspicious of BackPropagation

That something else, call it imagination or call it dreaming, does not require validation with immediate reality. The closest incarnation we have today is the generative adversarial network (GAN). A GAN consists of two networks, a generator and a discriminator. One can consider a discriminator as a neural network that acts in concert with the objective function. That is, it validates an internal generator network with reality. The generator is an automation that recreates an approximation of reality. A GAN works using back-propagation and it does perform unsupervised learning. So perhaps unsupervised learn doesn’t require an objective function, however it may still need back-propagation.

via https://medium.com/intuitionmachine/the-deeply-suspicious-nature-of-backpropagation-9bed5e2b085e

[enriquepage]

I listened to this talk almost a year ago, now that I am much more fluent at coding I went back to look into it since honestly the last time I saw it it only made sense conceptually, as soon as I had to start to code it I was totally out of my comfort zone and just couldn’t deliver at all.  Now that I’ve seen this again I get the impression that I can probably try and code something similar this time. Ideally (this was the original idea however it might have to change if this does not result being of relative ease to implement), the enemies in my game would have Artificial Intelligence generated by a Neural Network through the Back Propagation method, which is the simplest to implement and ironically also the only one that is 100% mathematically correct. This means no other method of generating Artificial Intelligence as of today, is considered “scientific” in it’s entirety (meaning the other ones are usually not accepted in the Academia since there are still no proofs of their validity). I will try to work this out to see if it is doable, since doing the AI for my game this way would save me ENORMOUS amounts of time, since it would mean I wouldn’t have to code it entirely on my own (as you probably can imagine, coding Artificial Intelligence  “the old way” is extremely slow, tedious, and also really hard). I will update you guys on the results of any proof of concept I work on!