- An artificial neural network (ANN) is a computational model inspired by biological neural networks in the brain. ANNs contain interconnected nodes that can learn relationships and patterns from data using a process similar to biological learning.
- The basic ANN architecture consists of an input layer, hidden layers, and an output layer. Information flows from the input to output layers through the hidden layers as the network learns.
- There are different types of ANNs that vary in their structure and learning methods, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks. ANNs can perform tasks using supervised, unsupervised, or reinforcement learning.
- ANNs have many applications including face recognition, ridesharing, handwriting
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.
This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work.
Below topics are explained in this neural network presentation:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
Learn more at: https://www.simplilearn.com
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
Classification by back propagation, multi layered feed forward neural network...bihira aggrey
Classification by Back Propagation, Multi-layered feed forward Neural Networks - Provides a basic introduction of classification in data mining with neural networks
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
What Is A Neural Network? | How Deep Neural Networks Work | Neural Network Tu...Simplilearn
This Neural Network presentation will help you understand what is deep learning, what is a neural network, how deep neural network works, advantages of neural network, applications of neural network and the future of neural network. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Deep Learning forms the basis for most of the incredible advances in Machine Learning. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. Now, let us deep dive into this video to understand how a neural network actually works along with some real-life examples.
Below topics are explained in this neural network presentation:
1. What is Deep Learning?
2. What is an artificial network?
3. How does neural network work?
4. Advantages of neural network
5. Applications of neural network
6. Future of neural network
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
Learn more at: https://www.simplilearn.com
In information technology (IT), a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks -- also called artificial neural networks -- are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI.
This presentation Neural Network will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a use case implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into these slides to understand how a neural network actually work.
Below topics are explained in this neural network presentation:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms.
Learn more at: https://www.simplilearn.com
This presentation provides an introduction to the artificial neural networks topic, its learning, network architecture, back propagation training algorithm, and its applications.
Classification by back propagation, multi layered feed forward neural network...bihira aggrey
Classification by Back Propagation, Multi-layered feed forward Neural Networks - Provides a basic introduction of classification in data mining with neural networks
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
What Is A Neural Network? | How Deep Neural Networks Work | Neural Network Tu...Simplilearn
This Neural Network presentation will help you understand what is deep learning, what is a neural network, how deep neural network works, advantages of neural network, applications of neural network and the future of neural network. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Deep Learning forms the basis for most of the incredible advances in Machine Learning. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. Now, let us deep dive into this video to understand how a neural network actually works along with some real-life examples.
Below topics are explained in this neural network presentation:
1. What is Deep Learning?
2. What is an artificial network?
3. How does neural network work?
4. Advantages of neural network
5. Applications of neural network
6. Future of neural network
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
Learn more at: https://www.simplilearn.com
In information technology (IT), a neural network is a system of hardware and/or software patterned after the operation of neurons in the human brain. Neural networks -- also called artificial neural networks -- are a variety of deep learning technology, which also falls under the umbrella of artificial intelligence, or AI.
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 .
This PPT contains entire content in short. My book on ANN under the title "SOFT COMPUTING" with Watson Publication and my classmates can be referred together.
اسلایدهای درس شبکه عصبی و یادگیری عمیق که در دانشگاه شیراز توسط استاد اقبال منصوری تدریس می شود.
Neural network and deep learning course slide taught by Professor Iqbal Mansouri at Shiraz University.
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
4. 4
• A neuron carries electrical impulses. They are the basic units of the nervous system
and its most important part is the brain.
• Dendrite — It receives signals from other neurons.
• Soma (cell body) — It sums all the incoming signals to generate input.
• Axon — When the sum reaches a threshold value, neuron fires and the signal travels
down the axon to the other neurons.
• Synapses — The point of interconnection of one neuron with other neurons. The
amount of signal transmitted depend upon the strength (synaptic weights) of the
connections.
BIOLOGICAL NEURON MODEL
5. ARTIFICIAL NEURAL NETWORKS
• An artificial neural network (ANN) is a
computational model based on the
structure and functions of biological
neural networks. Information that
flows through the network affects the
structure of the ANN because a neural
network changes - or learns, in a sense
- based on that input and output.
• ANNs are considered nonlinear
statistical data modeling tools where
the complex relationships between
inputs and outputs are modeled or
patterns are found.
• ANN is also known as a neural network.
5
6. 6
• INPUT LAYER — contains those units which
receive input from the outside world on
which network will learn, recognize and
processed.
• OUTPUT LAYER — contains units that respond
to the information about how it’s learned
any task.
• HIDDEN LAYER — These units are in between
input and output layers. The job of hidden
layer is to transform the input into something
that output unit can use in some way.
• In most neural networks , hidden neuron is
fully connected to the every neuron in its
previous layer(input) and to the next layer
(output) layer.
ANN ARCHITECTURE
8. • Supervised Learning— The training data is
input to the network, and the desired output
is known weights are adjusted until output
yields desired value.
• Unsupervised Learning— The input data is
used to train the network whose output is
known. The network classifies the input data
and adjusts the weight by feature extraction
in input data.
• Reinforcement Learning— Here the value of
the output is unknown, but the network
provides the feedback whether the output is
right or wrong. It is semi-supervised learning.
8
LEARNING
10. Storing information on the entire network
Ability to work with incomplete knowledge
Having fault tolerance
Parallel processing capability
Having a memory distribution
10
ADVANTAGES
11. DISADVANTAGES
• Hardware dependence
• Unrecognized behavior of the
network
• The duration of the network is
unknown
• Difficulty of showing the issue to
the network
11
12. CONCLUSION
12
• Artificial neural networks are inspired by the learning processes that take place in
biological systems.
• Biological neural learning happens by the modification of the synaptic strength.
Artificial neural networks learn in the same way.
• The synapse strength modification rules for artificial neural networks can be derived
by applying mathematical optimisation methods.
• Learning tasks of artificial neural networks can be reformulated as function
approximation tasks.
• Neural networks can be considered as nonlinear function approximating tools (i.e.,
linear combinations of nonlinear basis functions), where the parameters of the
networks should be found by applying optimisation methods.
• The optimisation is done with respect to the approximation error measure.
• In general it is enough to have a single hidden layer neural network (MLP, RBF or
other) to learn the approximation of a nonlinear function. In such cases general
optimisation can be applied to find the change rules for the synaptic weights.