The document discusses neural networks and their applications. It provides an overview of neural networks, including their history and how they are modeled after biological neurons. Supervised learning is described as training neural networks using labeled input-output pairs. Specific neural network concepts like the perceptron, backpropagation, and convolutional neural networks are explained. Applications mentioned include mobile computing, forecasting, character recognition, data mining, and image recognition. Both merits like flexibility and demerits like requiring large processing are noted.
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
An overview of Deep Learning With Neural Networks. Use cases of Deep learning and it's development. Basic introduction tp the layers of Neural Networks.
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
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.
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....
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
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
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.
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....
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Artificial Neural Networks: Applications In ManagementIOSR Journals
With the advancement of computer and communication technology, the tools used for management decisions have undergone a gigantic change. Finding the more effective solution and tools for managerial problems is one of the most important topics in the management studies today. Artificial Neural Networks (ANNs) are one of these tools that have become a critical component for business intelligence. The purpose of this article is to describe the basic behavior of neural networks as well as the works done in application of the same in management sciences and stimulate further research interests and efforts in the identified topics.
Neural network based numerical digits recognization using nnt in matlabijcses
Artificial neural networks are models inspired by human nervous system that is capable of learning. One of
the important applications of artificial neural network is character Recognition. Character Recognition
finds its application in number of areas, such as banking, security products, hospitals, in robotics also.
This paper is based on a system that recognizes a english numeral, given by the user, which is already
trained on the features of the numbers to be recognized using NNT (Neural network toolbox) .The system
has a neural network as its core, which is first trained on a database. The training of the neural network
extracts the features of the English numbers and stores in the database. The next phase of the system is to
recognize the number given by the user. The features of the number given by the user are extracted and
compared with the feature database and the recognized number is displayed.
Gives an Introduction to Deep learning, What can you achieve with deep learning. What is deep learning's relationship with machine learning. Technical basics of working of deep learning. Introduction to LSTM. How LSTM can be used for Text classification. Results obtained.. Practical recommendations.
Open CV Implementation of Object Recognition Using Artificial Neural Networksijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
This presentation deals with the basics of AI and it's connection with neural network. Additionally, it explains the pros and cons of AI along with the applications.
To be honest, this work is done for the purpose of building self confidence in me, based on my interest. Being Electronics student it gives enough courage to explore more on Machine Learning and Artificial Intelligence topics.
Thankyou for viewing and please leave a like to elevate my Confidence.
To add-on, this my first work on Slideshare.
Happy Learning
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
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.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
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.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
2. 2
╸ The history of neural networks begins before the invention of
computer i.e. , in 1943.
╸ The first neural network construction is done by neurologist for
understanding the working of neurons.
╸ Later technologists are also interested in this networks.
╸ In recent years, the importance of neural networks was observed.
History of Neural Networks
3. What are neural networks?
╸ In information technology (IT), an artificial neural network (ANN) is a system of
hardware and/or software patterned after the operation of neurons in the human
brain. ANNs -- also called, simply, neural networks -- are a variety of deep
learning technology, which also falls under the umbrella of artificial intelligence,
or AI.
╸ Commercial applications of these technologies generally focus on solving
complex signal processing or pattern recognition problems. Examples of
significant commercial applications since 2000 include handwriting recognition
for check processing, speech-to-text transcription, oil-exploration data analysis,
weather prediction and facial recognition.
3
4. Working of Biological neuron:
╸ A biological neuron contains mainly four parts. They are
dendrites, cell body, axon and synapse.
4
5. Working of Artificial neuron:
╸ An artificial neuron also contains dendrites, cell body, axon and synapse.
╸ In artificial neural network, the inputs are taken only when threshold value
is satisfied. Otherwise inputs are not taken by the neuron.
╸ There are two modes of neurons such as, training mode and using mode.
5
6. Receives n- inputs.
Multiplies each input by its weight.
Applies activation function to the sum of
results.
Outputs result.
7. Supervised learning
7
Supervised learning, as the name indicates, has the presence of a supervisor as a teacher. Basically
supervised learning is when we teach or train the machine using data that is well labelled. Which means some
data is already tagged with the correct answer. After that, the machine is provided with a new set of
examples(data) so that the supervised learning algorithm analyses the training data(set of training examples)
and produces a correct outcome from labelled data.
For instance, suppose you are given a basket filled with different kinds of fruits. Now the first step is to train
the machine with all the different fruits one by one like this:
If the shape of the object is rounded and has a depression at the top, is red in color, then it will be labeled as –
Apple.
If the shape of the object is a long curving cylinder having Green-Yellow color, then it will be labeled as –
Banana.
8. 8
- Supervised learning is the types of machine learning in which machines are trained using well "labelled"
training data, and on basis of that data, machines predict the output. The labelled data means some input
data is already tagged with the correct output.
In supervised learning, the training data provided to the machines work as the supervisor that teaches the
machines to predict the output correctly. It applies the same concept as a student learns in the supervision of
the teacher.
Supervised learning is a process of providing input data as well as correct output data to the machine
learning model. The aim of a supervised learning algorithm is to find a mapping function to map the input
variable(x) with the output variable(y).
In the real-world, supervised learning can be used for Risk Assessment, Image classification, Fraud
Detection, spam filtering, etc.
- Supervised learning (SL) is a machine learning paradigm for problems where the available data consists
of labelled examples, meaning that each data point contains features (covariates) and an associated label.
The goal of supervised learning algorithms is learning a function that maps feature vectors (inputs) to labels
(output), based on example input-output pairs.[1] It infers a function from labeled training data consisting of a
set of training examples.[2] In supervised learning, each example is a pair consisting of an input object
(typically a vector) and a desired output value (also called the supervisory signal).
SUPERVISED LEARNING
9. Single Layer Perceptron
╸ The perceptron is a single processing unit of any neural network. Frank
Rosenblatt first proposed in 1958 is a simple neuron which is used to classify its input
into one or two categories. Perceptron is a linear classifier, and is used in supervised
learning. It helps to organize the given input data.
╸ A perceptron is a neural network unit that does a precise computation to detect
features in the input data. Perceptron is mainly used to classify the data into two
parts. Therefore, it is also known as Linear Binary Classifier.
╸ Perceptron uses the step function that returns +1 if the weighted sum of its input 0
and -1.
╸ The activation function is used to map the input between the required value like (0, 1)
or (-1, 1).
9
11. What is Backpropagation?
9
Backpropagation is the essence of neural network training. It is the method of fine-tuning the
weights of a neural network based on the error rate obtained in the previous epoch (i.e.,
iteration). Proper tuning of the weights allows you to reduce error rates and make the model
reliable by increasing its generalization.
Backpropagation in neural network is a short form for “backward propagation of errors.” It is a
standard method of training artificial neural networks. This method helps calculate the gradient
of a loss function with respect to all the weights in the network.
Most prominent advantages of Backpropagation are:
Backpropagation is fast, simple and easy to program
It has no parameters to tune apart from the numbers of input
It is a flexible method as it does not require prior knowledge about the network
It is a standard method that generally works well
It does not need any special mention of the features of the function to be learned.
12. 12
How Supervised Learning Works?
In supervised learning, models are trained using labelled dataset, where the model
learns about each type of data. Once the training process is completed, the model is
tested on the basis of test data (a subset of the training set), and then it predicts the
output.
The working of Supervised learning can be easily understood by the below example
and diagram shown in next slide.
Suppose we have a dataset of different types of shapes which includes
square, rectangle, triangle, and Polygon. Now the first step is that we need
to train the model for each shape.
If the given shape has four sides, and all the sides are equal, then it will be
labelled as a Square.
If the given shape has three sides, then it will be labelled as a triangle.
If the given shape has six equal sides then it will be labelled as hexagon.
Now, after training, we test our model using the test set, and the task of
the model is to identify the shape.
The machine is already trained on all types of shapes, and when it finds a
new shape, it classifies the shape on the bases of a number of sides, and
predicts the output.
14. Application of neural networks
- Neural networks have broad applicability to real world business problems.
In fact, they have already been successfully applied in many industries.
- Mobile computing
- Forecasting
- Character recognition
- Traveling salesman problem
- Data mining
- Game development
- Pattern recognition
14
15. 15
ACC are also used in the following specific paradigms:
- Recognition of speakers in communication;
- Hand-written word recognition and
- Face recognition.
Pattern Recognition
18. 18
Image recognition by CNN
One of the most popular techniques used in improving the accuracy of image classification Is
convolutional Neural Networks (CNNs for short).
Instead of feeding the entire image as an array of numbers, the image is broken up into a number of
Tiles, the machines then tries to predict what each tile is.
Finally, the computer tries to predict what’s in the picture based on the prediction of all the titles.
This allows the computer to parallelize the operations and detect the object regardless of where it is
located in the Image.
19. Merits:
No need to write aya algorithms.
Work by learning.
Work will be automatically shared.
Robust.
Neural networks works efficiently.
19
20. De-merits:
1. Needs to understand before working with neural networks.
2. Requires high processing time for large neural networks.
3. Noisy data.
4. Takes large time for connecting neurons.
21. Conclusion
- The computer world has a lot to gain from neural networks.
- Their ability to learn by example makes them very flexible and powerful
- They are also very well suited for real time system
- Neural networks also contribute to other areas of research such as neurology and
psychology
- Finally, I would link to state that even though neural networks have a huge potential
we will only get the best of them.
When they are integrated with computing, AI, fuzzy logic and related subjects.