This document describes Bidirectional Associative Memory (BAM), a supervised learning model in artificial neural networks. BAM is able to store hetero-associative pattern pairs and retrieve a pattern given an incomplete or noisy input pattern. It has an architecture that accepts an n-dimensional input vector from set A and recalls an m-dimensional output vector from set B, and vice versa. The algorithm involves learning weight matrices between pattern pairs and testing recall accuracy. An example demonstrates storing and retrieving 4 input-output pattern pairs using a BAM with 6 input and 3 output neurons. Applications include pattern recognition and image processing.
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
Blind equalization is a digital signal processing technique in which the transmitted signal is inferred (equalized) from the received signal, while making use only of the transmitted signal statistics. Hence, the use of the word blind in the name.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
The presentation material for the reading club of Pattern Recognition and Machine Learning by Bishop.
The contents of the section cover
- EM algorithm for HMM
- Forward-Backward Algorithm
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研究室でのBishop著『パターン認識と機械学習』(PRML)の輪講用発表資料(ぜんぶ英語)です。
担当範囲は
・隠れマルコフモデルに対するEMアルゴリズムのEステップ
・フォワード-バックワードアルゴリズム
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
This is very simple introduction to Clustering with some real world example. At the end of lecture I use stackOverflow API to test some clustering. I also wants to try facebook but it has some problem with it's API
Blind equalization is a digital signal processing technique in which the transmitted signal is inferred (equalized) from the received signal, while making use only of the transmitted signal statistics. Hence, the use of the word blind in the name.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
The presentation material for the reading club of Pattern Recognition and Machine Learning by Bishop.
The contents of the section cover
- EM algorithm for HMM
- Forward-Backward Algorithm
-------------------------------------------------------------------------
研究室でのBishop著『パターン認識と機械学習』(PRML)の輪講用発表資料(ぜんぶ英語)です。
担当範囲は
・隠れマルコフモデルに対するEMアルゴリズムのEステップ
・フォワード-バックワードアルゴリズム
FellowBuddy.com is an innovative platform that brings students together to share notes, exam papers, study guides, project reports and presentation for upcoming exams.
We connect Students who have an understanding of course material with Students who need help.
Benefits:-
# Students can catch up on notes they missed because of an absence.
# Underachievers can find peer developed notes that break down lecture and study material in a way that they can understand
# Students can earn better grades, save time and study effectively
Our Vision & Mission – Simplifying Students Life
Our Belief – “The great breakthrough in your life comes when you realize it, that you can learn anything you need to learn; to accomplish any goal that you have set for yourself. This means there are no limits on what you can be, have or do.”
Like Us - https://www.facebook.com/FellowBuddycom
ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
An ensemble is itself a supervised learning algorithm, because it can be trained and then used to make predictions. The trained ensemble, therefore, represents a single hypothesis. This hypothesis, however, is not necessarily contained within the hypothesis space of the models from which it is built.
This video presents the next step of automated pipe of Machine Learning implemented by Edge-ML. The aim is to build multi-varied classifier without parameters to optimise, starting from mono-varied pre-treatments (discretisation and grouping). More precisely, this talk presents 4 ways of improvement of the Naive Bayes classifier, in order to make this classifier more accurate while keeping its robustness.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
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Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
2. TABLE OF CONTENT
• 1. INTRODUCTION
• 2.OBJECTIVE OF BAM
• 3.ARCHITECTURE
• 4. ALGORITHM
• 5.EXAMPLE BASED BAM
• 6.APPLICATION
• 7. LIMITATION
• 8.REFERENCES
3. INTRODUCTION
• Bidirectional Associative Memory (BAM) is a supervised learning model in Artificial
Neural Network.
• This is hetero-associative memory, for an input pattern, it returns another pattern which
is potentially of a different size
• A Recurrent Neural Network (RNN) is needed to receive a pattern of one set of neurons
as an input and generate a related, but different, output pattern of another set of neurons.
4. OBJECTIVE OF BAM
• A network model is to store hetero-associative pattern pairs
• This is used to retrieve a pattern given a noisy or incomplete pattern.
5. ARCHITECTURE
• When BAM accepts an input of n-dimensional vector X from set A then the model
recalls m-dimensional vector Y from set B. Similarly when Y is treated as input, the BAM
recalls X.
6. ALGORITHM
• 1. Storage (Learning): In this learning step of BAM, weight matrix is calculated between
M pairs of patterns (fundamental memories) are stored in the synaptic weights of the network
following the equation
7. 2. Testing: We have to check that the BAM recalls perfectly for corresponding and
recalls for corresponding Using,
All pairs should be recalled accordingly.
3. Retrieval: For an unknown vector X (a corrupted or incomplete version of a pattern from
set A or B) to the BAM and retrieve a previously stored association
8. o Initialize the BAM:
o
o Calculate the BAM output at iteration :
o
o Update the input vector :
o
o Repeat the iteration until convergence, when input and output remain unchanged.
9. EXAMPLE BASED BAM
• Set A: Input Patterns
• Set B: Corresponding Target Patterns
10. Step 1: Here, the value of M (no of pairs of patterns) is 4.
Step 2: Assign the neurons in the input and output layer. Here, neurons in the input layer are 6
and the output layer are 3
Step 3: Now, compute the Weight Matrix (W):
11. Step 4: Test the BAM model learning algorithm- for the input patterns BAM will return the
corresponding target patterns as output. And for each of the target patterns, BAM will return the
corresponding input patterns.
•Test on input patterns (Set A) using-
12. Test on target patterns (Set B) using -
Here, for each of the input patterns, the BAM model will give correct target patterns and for target patterns, the
model will also give corresponding input patterns.
This signifies the bidirectional association in memory or model network.
14. LIMITATION
• Storage capacity of the BAM: In the BAM, stored number of
associations should not be exceeded the number of neurons in the smaller
layer.
• Incorrect convergence: Always the closest association may not be
produced by BAM.