The document discusses artificial neural networks. It describes their basic structure and components, including dendrites that receive input signals, a soma that processes the inputs, and an axon that transmits output signals. It also explains how neurons are connected at synapses to transfer signals between neurons. Finally, it mentions different types of activation functions that can be used in neural networks.
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.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Artificial Intelligence: Artificial Neural NetworksThe Integral Worm
This presentation covers artificial neural networks for artificial intelligence. Topics covered are as follows: artificial neural networks, basic representation, hidden units, exclusive OR problem, backpropagation, advantages of artificial neural networks, properties of artificial neural networks, and disadvantages of artificial neural networks.
Basics of Neural networks and its image recognition and its applications of engineering fields and medicines and how it detect those images and give the results of those images....
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. It works on real-valued, discrete-valued and vector valued.
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Artificial Intelligence: Artificial Neural NetworksThe Integral Worm
This presentation covers artificial neural networks for artificial intelligence. Topics covered are as follows: artificial neural networks, basic representation, hidden units, exclusive OR problem, backpropagation, advantages of artificial neural networks, properties of artificial neural networks, and disadvantages of artificial neural networks.
Basics of Neural networks and its image recognition and its applications of engineering fields and medicines and how it detect those images and give the results of those images....
An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. It works on real-valued, discrete-valued and vector valued.
I think this could be useful for those who works in the field of Coputational Intelligence. Give your valuable reviews so that I can progree in my research
Artificial Neural Network in a Tic Tac Toe Symfony Console Application - Symf...aferrandini
Among all the C libraries bindings PHP offers, there is one for FANN: Fast Artificial Neural Network (libfann). With it you can easily create a neural Network with different activation functions for each neuron/ layer. ANNs (Artificial Neural Networks) are used for machine learning and for recommendation systems. In this Talk we will show an implementation (and running demo) of a basic IA that will learn to play Tic Tac Toe leveraging the Symfony console componente as UI While we demo, we will show a detailed log of what is going on.
SymfonyCon Madrid 2014
Artificial Neural Network Seminar - Google BrainRawan Al-Omari
it's our seminar in artificial neural network course, at F.I.T.E, AI Dept.
it's about Google Brain project, and who they using neural network in building it .
actually it's a very interesting project they work on it .
for more information about this project :
http://nyti.ms/T5E71e
Brief and overall introduction to Artificial Neural Network (ANN).
-history of ANN
-learning technique (backpropagation)
-Generations of Neural net from 1st to 3rd
Introduction to Artificial Neural Network Qingkai Kong
This is the slides I created for the workshop at Berkeley D-Lab - Introduction to Artificial Neural Networks (ANN). It consists the basics of ANN, intuitive examples, and python implementation of the ANN. You can find rest of the materials (notebooks) at https://github.com/qingkaikong/20161202_ANN_basics.
Deep Learning Tutorial | Deep Learning TensorFlow | Deep Learning With Neural...Simplilearn
This Deep Learning presentation will help you in understanding what is Deep Learning, why do we need Deep learning, what is neural network, applications of Deep Learning, what is perceptron, implementing logic gates using perceptron, types of neural networks. At the end of the video, you will get introduced to TensorFlow along with a usecase implementation on recognizing hand-written digits. Deep Learning is inspired by the integral function of the human brain specific to artificial neural networks. These networks, which represent the decision-making process of the brain, use complex algorithms that process data in a non-linear way, learning in an unsupervised manner to make choices based on the input. Deep Learning, on the other hand, uses advanced computing power and special type of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. W will also understand neural networks and how they work in this Deep Learning tutorial video. This Deep Learning tutorial is ideal for professionals with beginner to intermediate level of experience. Now, let us dive deep into this topic and understand what Deep Learning actually is.
Below topics are explained in this Deep Learning presentation:
1. What is Deep Learning?
2. Why do we need Deep Learning?
3. What is Neural network?
4. What is Perceptron?
5. Implementing logic gates using Perceptron
6. Types of Neural networks
7. Applications of Deep Learning
8. Working of Neural network
9. Introduction to TensorFlow
10. Use case implementation using TensorFlow
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.
There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in deep learning
explain backpropagation with a simple example.
normally, we use cross-entropy as loss function.
and we set the activation function of the output layer as the logistic sigmoid. because we want to maximize (log) likelihood. (or minimize negative (log) likelihood), and we suppose that the function is a binomial distribution which is the maximum entropy function in two-class classification.
but in this example, we set the loss function (objective function or cost function) as sum of square, which is normally used in logistic regression, for simplifying the problem.
An artificial neural network (ANN), often just called a "neural network" (NN), is a mathematical model or computational model based on biological neural networks, in other words, is an emulation of biological neural system.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
تحلیل سیستم پاداش و عملکرد مدیریت جدید پاداش و طرح ریزی استراتژیک نظام پاداشVajiheh Zoghiyan
از نگاه مایکل آرمسترانگ با ترجمه دکتر خدایار ابیلی و حسن موفقی و از منظر ریچارد ام.ستیرز و لیمان دبلیو. پورتر با ترجمه سید امین الله علوی و از منظر ادوارد ای. لالر با ترجمه سید امین الله علوی
Organizational analysis of Ministry of Youth and Sports in IranVajiheh Zoghiyan
this file is an organizational analysis about Ministry of Youth and Sports in Iran, organization flowchart, strength and weaknesses of this Ministry, structure, values, vision, Mission
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.
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.
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.
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.
8. • Command line solution
• Graphical user interface: nftool
• nntool
8
There are 3 simple ways to solve a neural network
problem in Matlab
Gathered by: Vajiheh Zoghiyan