#back propagation

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…

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.

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 – – –…

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

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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

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.