A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.
This presentation is based on new trends and technology in computer science. In this presentation, we have depicted the basic principles of artificial intelligence. An attempt has been made to explain advanced technologies like machine learning and deep learning.
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
This presentation is based on new trends and technology in computer science. In this presentation, we have depicted the basic principles of artificial intelligence. An attempt has been made to explain advanced technologies like machine learning and deep learning.
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Deep learning is now making the Artificial Intelligence near to Human. Machine Learning and Deep Artificial Neural Network make the copy of Human Brain. The success is due to large storage, computation with efficient algorithms to handle more behavioral and cognitive problem
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
A public member is visible from anywhere in the system. In class diagram, it is prefixed by the symbol '+'. Private − A private member is visible only from within the class.
the compression of images is an important step before we start the processing of larger images or videos. The compression of images is carried out by an encoder and output a compressed form of an image. In the processes of compression, the mathematical transforms play a vital role.
Artificial neural networks, usually simply called neural networks, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain.
Artificial Neural Network and its Applicationsshritosh kumar
Abstract
This report is an introduction to Artificial Neural
Networks. The various types of neural networks are
explained and demonstrated, applications of neural
networks like ANNs in medicine are described, and a
detailed historical background is provided. The
connection between the artificial and the real thing is
also investigated and explained. Finally, the
mathematical models involved are presented and
demonstrated.
Deep learning is now making the Artificial Intelligence near to Human. Machine Learning and Deep Artificial Neural Network make the copy of Human Brain. The success is due to large storage, computation with efficient algorithms to handle more behavioral and cognitive problem
Deep Learning - The Past, Present and Future of Artificial IntelligenceLukas Masuch
In the last couple of years, deep learning techniques have transformed the world of artificial intelligence. One by one, the abilities and techniques that humans once imagined were uniquely our own have begun to fall to the onslaught of ever more powerful machines. Deep neural networks are now better than humans at tasks such as face recognition and object recognition. They’ve mastered the ancient game of Go and thrashed the best human players. “The pace of progress in artificial general intelligence is incredible fast” (Elon Musk – CEO Tesla & SpaceX) leading to an AI that “would be either the best or the worst thing ever to happen to humanity” (Stephen Hawking – Physicist).
What sparked this new hype? How is Deep Learning different from previous approaches? Let’s look behind the curtain and unravel the reality. This talk will introduce the core concept of deep learning, explore why Sundar Pichai (CEO Google) recently announced that “machine learning is a core transformative way by which Google is rethinking everything they are doing” and explain why “deep learning is probably one of the most exciting things that is happening in the computer industry“ (Jen-Hsun Huang – CEO NVIDIA).
A public member is visible from anywhere in the system. In class diagram, it is prefixed by the symbol '+'. Private − A private member is visible only from within the class.
the compression of images is an important step before we start the processing of larger images or videos. The compression of images is carried out by an encoder and output a compressed form of an image. In the processes of compression, the mathematical transforms play a vital role.
Supervised learning is a machine learning approach that's defined by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately.
A web service is either: a service offered by an electronic device to another electronic device, communicating with each other via the Internet, or a server running on a computer device, listening for requests at a particular port over a network, serving web documents.
Entrepreneurship is the ability and readiness to develop, organize and run a business enterprise, along with any of its uncertainties in order to make a profit. The most prominent example of entrepreneurship is the starting of new businesses.
The IoT design approach is an approach to the IoT system development with respect to the peculiarities of the Internet of Things. That term covers all components of IoT architecture from IoT devices and their hardware to applications and user interfaces.
DCOM (Distributed Component Object Model) and CORBA (Common Object Request Broker Architecture) are two popular distributed object models. In this paper, we make architectural comparison of DCOM and CORBA at three different layers: basic programming architecture, remoting architecture, and the wire protocol architecture.
Tweepy is an open source Python package that gives you a very convenient way to access the Twitter API with Python. Tweepy includes a set of classes and methods that represent Twitter's models and API endpoints, and it transparently handles various implementation details, such as: Data encoding and decoding.
DCOM (Distributed Component Object Model) and CORBA (Common Object Request Broker Architecture) are two popular distributed object models. In this paper, we make architectural comparison of DCOM and CORBA at three different layers: basic programming architecture, remoting architecture, and the wire protocol architecture.
The learning method used by our approach is usually known in the artificial intelligence community as learning by observation, imitation learning, learning from demonstration, programming by demonstration, learning by watching or learning by showing. For consistency, learning by observation will be used from here on.
Every application has some basic interactive interface for the user. For example, a button, check-box, radio-button, text-field, etc. These together form the components in Swing.
Distributed firewall is an mechanisms to enforce a network domain security policy through the use of policy language.
Security policy is defined centrally.
A multiprocessor system consists of multiple processing units connected via some interconnection network plus the software needed to make the processing units work together.
The traveling salesman problem (TSP) is an algorithmic problem tasked with finding the shortest route between a set of points and locations that must be visited.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
1. NADAR SARASWATHI COLLEGE OF ARTS AND SCIENCE,
THENI
DEPARTMENT OF INFORMATION TECHNOLOGY
soft computing
NAME: G.KAVIYA
CLASS: MSC(IT)
TOPIC: NEURAL NETWORK
2. WHAT IS A NEURAL NETWORK?
• A neural network is a method in artificial intelligence that
teaches computers to process data in a way that is inspired by
the human brain. It is a type of machine learning process,
called deep learning, that uses interconnected nodes or neurons
in a layered structure that resembles the human brain.
• It creates an adaptive system that computers use to learn from
their mistakes and improve continuously. Thus, artificial
neural networks attempt to solve complicated problems, like
summarizing documents or recognizing faces, with greater
accuracy.
3. WHY ARE NEURAL NETWORKS
IMPORTANT?
• Neural networks can help computers make
intelligent decisions with limited human
assistance. This is because they can learn and
model the relationships between input and
output data that are nonlinear and complex.
For instance, they can do the following tasks.
4. MAKE GENERALIZATIONS AND
INFERENCES
• Neural networks can comprehend unstructured data
and make general observations without explicit
training. For instance, they can recognize that two
different input sentences have a similar meaning:
• Can you tell me how to make the payment?
• How do I transfer money?
• A neural network would know that both sentences
mean the same thing. Or it would be able to broadly
recognize that Baxter Road is a place, but Baxter
Smith is a person’s name.
5. WHAT ARE NEURAL NETWORKS
USED FOR?
• Neural networks have several use cases across many
industries, such as the following:
• Medical diagnosis by medical image classification
• Targeted marketing by social network filtering and
behavioral data analysis
• Financial predictions by processing historical data of
financial instruments
• Electrical load and energy demand forecasting
• Process and quality control
• Chemical compound identification
6. COMPUTER VISION
• Computer vision is the ability of computers to
extract information and insights from images
and videos. With neural networks, computers
can distinguish and recognize images similar
to humans. Computer vision has several
applications, such as the following:
• Visual recognition in self-driving cars so they
can recognize road signs and other road users.
7. CONTINUE
• Content moderation to automatically remove
unsafe or inappropriate content from image
and video archives
• Facial recognition to identify faces and
recognize attributes like open eyes, glasses,
and facial hair
• Image labeling to identify brand logos,
clothing, safety gear, and other image details
8. SPEECH RECOGNITION
• Neural networks can analyze human speech despite
varying speech patterns, pitch, tone, language, and
accent. Virtual assistants like Amazon Alexa and
automatic transcription software use speech recognition
to do tasks like these:
• Assist call center agents and automatically classify calls
• Convert clinical conversations into documentation in
real time
• Accurately subtitle videos and meeting recordings for
wider content reach
9. NATURAL LANGUAGE PROCESSING
• Natural language processing (NLP) is the ability to process
natural, human-created text. Neural networks help
computers gather insights and meaning from text data and
documents. NLP has several use cases, including in these
functions:
• Automated virtual agents and chatbots
• Automatic organization and classification of written data
• Business intelligence analysis of long-form documents like
emails and forms
• Indexing of key phrases that indicate sentiment, like
positive and negative comments on social media
• Document summarization and article generation for a given
topic
10. HOW DO NEURAL NETWORKS
WORK?
• The human brain is the inspiration behind neural network
architecture. Human brain cells, called neurons, form a
complex, highly interconnected network and send electrical
signals to each other to help humans process information.
Similarly, an artificial neural network is made of artificial
neurons that work together to solve a problem.
• Artificial neurons are software modules, called nodes, and
artificial neural networks are software programs or algorithms
that, at their core, use computing systems to solve
mathematical calculations.
11. LAYERS TYPES
• Simple neural network architecture
• A basic neural network has interconnected artificial
neurons in three layers:
• Input Layer
• Information from the outside world enters the
artificial neural network from the input layer. Input
nodes process the data, analyze or categorize it, and
pass it on to the next layer.
12. CONTINUE
• HIDDEN LAYER
• Hidden layers take their input from the input layer or other hidden
layers. Artificial neural networks can have a large number of hidden
layers. Each hidden layer analyzes the output from the previous
layer, processes it further, and passes it on to the next layer.
• OUTPUT LAYER
• The output layer gives the final result of all the data processing by
the artificial neural network. It can have single or multiple nodes.
For instance, if we have a binary (yes/no) classification problem, the
output layer will have one output node, which will give the result as
1 or 0. However, if we have a multi-class classification problem, the
output layer might consist of more than one output node.
13. DEEP NEURAL NETWORK
ARCHITECTURE
• Deep neural networks, or deep learning networks,
have several hidden layers with millions of
artificial neurons linked together. A number,
called weight, represents the connections between
one node and another. The weight is a positive
number if one node excites another, or negative if
one node suppresses the other. Nodes with higher
weight values have more influence on the other
nodes.
14. CONTINUE
• Theoretically, deep neural networks can map
any input type to any output type. However,
they also need much more training as
compared to other machine learning methods.
They need millions of examples of training
data rather than perhaps the hundreds or
thousands that a simpler network might need.
15. What are the types of neural
networks?
• Artificial neural networks can be categorized by
how the data flows from the input node to the
output node. Below are some examples:
• FEEDFORWARD NEURAL NETWORKS
• Feedforward neural networks process data in one
direction, from the input node to the output node.
Every node in one layer is connected to every
node in the next layer. A feedforward network
uses a feedback process to improve predictions
over time.
16. FEEDFORWARD NEURAL
NETWORKS
• Feedforward neural networks process data in
one direction, from the input node to the
output node. Every node in one layer is
connected to every node in the next layer. A
feedforward network uses a feedback process
to improve predictions over time.
17. BACKPROPAGATION ALGORITHM
• Artificial neural networks learn continuously by
using corrective feedback loops to improve their
predictive analytics. In simple terms, you can
think of the data flowing from the input node to
the output node through many different paths in
the neural network. Only one path is the correct
one that maps the input node to the correct output
node. To find this path, the neural network uses a
feedback loop, which works as follows:
• Each node makes a guess about the next node in
the path.
18. CONTINUE
• It checks if the guess was correct. Nodes
assign higher weight values to paths that lead
to more correct guesses and lower weight
values to node paths that lead to incorrect
guesses.
• For the next data point, the nodes make a new
prediction using the higher weight paths and
then repeat Step 1.
19. CONVOLUTIONAL NEURAL
NETWORKS
• The hidden layers in convolutional neural
networks perform specific mathematical
functions, like summarizing or filtering, called
convolutions. They are very useful for image
classification because they can extract relevant
features from images that are useful for image
recognition and classification. The new form is
easier to process without losing features that are
critical for making a good prediction. Each hidden
layer extracts and processes different image
features, like edges, color, and depth.
20. HOW TO TRAIN NEURAL
NETWORKS?
• Neural network training is the process of
teaching a neural network to perform a task.
Neural networks learn by initially processing
several large sets of labeled or unlabeled data.
By using these examples, they can then
process unknown inputs more accurately.