The document provides an introduction to artificial neural networks. It discusses that artificial neural networks are modeled after biological neural networks in the human brain. They can be used to perform tasks like pattern recognition, decision making, and optimization by learning from large amounts of data in a similar way that the human brain learns. The document describes the basic building blocks of artificial neural networks, including artificial neurons, weights, biases, and different activation functions. It also discusses some early models of artificial neural networks like the McCulloch-Pitts neuron model.
The document summarizes research on developing efficient convolutional neural network architectures called MobileNets that are well-suited for mobile and embedded vision applications. The key ideas are using depthwise separable convolutions to factorize standard convolutions and using a width multiplier and resolution multiplier to control model size. Experiments show MobileNets achieve higher accuracy and speed than prior mobile networks on image classification and object detection tasks while having a smaller memory footprint.
The document provides an introduction to physics-informed machine learning. It discusses the limitations of traditional modeling approaches and machine learning alone. Physics-informed machine learning aims to embed physical laws and constraints into machine learning models. There are three main approaches: incorporating observational biases, inductive biases from physics, and learning biases like physics-informed neural networks (PINNs). PINNs have been applied to problems with complex geometries and different physical laws but can have convergence issues that require further research. Overall, physics-informed machine learning shows promise for improving simulations but many open problems remain.
This document provides an overview of quantum cryptography. It begins with definitions of cryptography and the history of quantum cryptography. Key concepts of quantum cryptography are then explained, including the Heisenberg uncertainty principle, photon polarization, and qubits. Common quantum cryptography protocols like BB84 and B92 are described. The document also discusses implementing quantum cryptography, advantages like virtually unhackable security, and disadvantages like high costs. In conclusion, quantum cryptography is presented as a promising technique for secure communication based on quantum physics.
Qiskit is an open source framework of Quantum Computing. It provides tools for creating and manipulating quantum programs and running them on prototype quantum devices on IBM Q Experience or on simulators on a local computer.
https://hasgeek.com/rootconf/data-privacy-conference/sub/synthetic-data-generation-VN92QpTzvTSAeepCW8YRMU
Synthetic data generation for relational data
per column density estimation
covariance
copula
Dendral was an early artificial intelligence system developed in the 1960s at Stanford University to help chemists identify unknown organic molecules. It used mass spectrometry data and knowledge of chemistry to generate possible molecular structures and test them against the data. Dendral consisted of two subprograms: Heuristic Dendral, which produced potential structures, and Meta Dendral, which learned to explain the correlation between structures and spectra. The system pioneered the use of heuristics, knowledge engineering, and the plan-generate-test problem-solving paradigm in expert systems.
[PR12] You Only Look Once (YOLO): Unified Real-Time Object DetectionTaegyun Jeon
The document summarizes the You Only Look Once (YOLO) object detection method. YOLO frames object detection as a single regression problem to directly predict bounding boxes and class probabilities from full images in one pass. This allows for extremely fast detection speeds of 45 frames per second. YOLO uses a feedforward convolutional neural network to apply a single neural network to the full image. This allows it to leverage contextual information and makes predictions about bounding boxes and class probabilities for all classes with one network.
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
The document summarizes research on developing efficient convolutional neural network architectures called MobileNets that are well-suited for mobile and embedded vision applications. The key ideas are using depthwise separable convolutions to factorize standard convolutions and using a width multiplier and resolution multiplier to control model size. Experiments show MobileNets achieve higher accuracy and speed than prior mobile networks on image classification and object detection tasks while having a smaller memory footprint.
The document provides an introduction to physics-informed machine learning. It discusses the limitations of traditional modeling approaches and machine learning alone. Physics-informed machine learning aims to embed physical laws and constraints into machine learning models. There are three main approaches: incorporating observational biases, inductive biases from physics, and learning biases like physics-informed neural networks (PINNs). PINNs have been applied to problems with complex geometries and different physical laws but can have convergence issues that require further research. Overall, physics-informed machine learning shows promise for improving simulations but many open problems remain.
This document provides an overview of quantum cryptography. It begins with definitions of cryptography and the history of quantum cryptography. Key concepts of quantum cryptography are then explained, including the Heisenberg uncertainty principle, photon polarization, and qubits. Common quantum cryptography protocols like BB84 and B92 are described. The document also discusses implementing quantum cryptography, advantages like virtually unhackable security, and disadvantages like high costs. In conclusion, quantum cryptography is presented as a promising technique for secure communication based on quantum physics.
Qiskit is an open source framework of Quantum Computing. It provides tools for creating and manipulating quantum programs and running them on prototype quantum devices on IBM Q Experience or on simulators on a local computer.
https://hasgeek.com/rootconf/data-privacy-conference/sub/synthetic-data-generation-VN92QpTzvTSAeepCW8YRMU
Synthetic data generation for relational data
per column density estimation
covariance
copula
Dendral was an early artificial intelligence system developed in the 1960s at Stanford University to help chemists identify unknown organic molecules. It used mass spectrometry data and knowledge of chemistry to generate possible molecular structures and test them against the data. Dendral consisted of two subprograms: Heuristic Dendral, which produced potential structures, and Meta Dendral, which learned to explain the correlation between structures and spectra. The system pioneered the use of heuristics, knowledge engineering, and the plan-generate-test problem-solving paradigm in expert systems.
[PR12] You Only Look Once (YOLO): Unified Real-Time Object DetectionTaegyun Jeon
The document summarizes the You Only Look Once (YOLO) object detection method. YOLO frames object detection as a single regression problem to directly predict bounding boxes and class probabilities from full images in one pass. This allows for extremely fast detection speeds of 45 frames per second. YOLO uses a feedforward convolutional neural network to apply a single neural network to the full image. This allows it to leverage contextual information and makes predictions about bounding boxes and class probabilities for all classes with one network.
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
DetectoRS for Object Detection/Segmentation
On COCO test-dev, DetectoRS achieves state-of-the art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation.
(2020.07)
Despite the increase of deep learning practitioners and researchers, many of them do not use GPUs, this may lead to long training/evaluation cycles and non-practical research.
In his talk, Lior shares how to get started with GPUs and some of the best practices that helped him during research and work. The talk is for everyone who works with machine learning (deep learning experience is NOT mandatory!), It covers the very basics of how GPU works, CUDA drivers, IDE configuration, training, inference, and multi-GPU training.
PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION ...Md Kafiul Islam
This document summarizes an oral defense presentation for a PhD dissertation on artifact characterization, detection, and removal from neural signals. The presentation outlines the background on in-vivo neural signals and EEG, problems and motivation regarding artifacts corrupting signals, thesis objectives, literature review on existing artifact removal methods, contributions of the dissertation including artifact study and proposed removal algorithms, and plans for future work. The presentation aims to investigate artifacts in neural data, develop automated detection and removal without distorting signals, evaluate methods, and improve applications like epilepsy detection and brain-computer interfaces.
The document summarizes the U-Net convolutional network architecture for biomedical image segmentation. U-Net improves on Fully Convolutional Networks (FCNs) by introducing a U-shaped architecture with skip connections between contracting and expansive paths. This allows contextual information from the contracting path to be combined with localization information from the expansive path, improving segmentation of biomedical images which often have objects at multiple scales. The U-Net architecture has been shown to perform well even with limited training data due to its ability to make use of context.
This document is a slide presentation on recent advances in deep learning. It discusses self-supervised learning, which involves using unlabeled data to learn representations by predicting structural information within the data. The presentation covers pretext tasks, invariance-based approaches, and generation-based approaches for self-supervised learning in computer vision and natural language processing. It provides examples of specific self-supervised methods like predicting image rotations, clustering representations to generate pseudo-labels, and masked language modeling.
The document summarizes a presentation on quantum information and technologies. It discusses:
1) How quantum computing could enable solving problems in fields like space science, biology, and finance faster than classical computers by taking advantage of quantum properties like superposition and entanglement.
2) Some of the basic concepts in quantum information like qubits, qudits, wavefunctions, error correction, and different methods for building quantum computers like superconducting and optical approaches.
3) The status of quantum computing including cloud access to quantum processors with over 100 qubits now available from IBM, though fully error corrected quantum computers still remain in development.
Erie Insurance Group interview questions and answersleybrad610
This document provides interview preparation materials for Erie Insurance Group, including answers to common interview questions, tips for researching the company, and additional job interview resources. Sample answers are given for questions like "What is your greatest weakness?" and "Why should we hire you?". The document emphasizes being honest, relating your strengths to the role, and asking the interviewer questions about the company and position. Additional links provide practice interview questions and a free ebook with sample answers.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
Slides from the UPC reading group on computer vision about the following paper:
Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).
This document outlines a talk on high-performance computing trends. It discusses the need for HPC, parallel architectures and systems, high-throughput computing, distributed computing models like grids and clouds, and examples like the Top500 supercomputers and Amazon Web Services. It also previews a question and answer session at the end.
Cyber Security and Post Quantum Cryptography By: Professor Lili SaghafiProfessor Lili Saghafi
Quantum computing has the potential to transform cybersecurity.
Some encryption algorithms are thought to be unbreakable, except by brute-force attacks.
Although brute-force attacks may be hard for classical computers, they would be easy for quantum computers making them susceptible to such attacks.
All financial institutions, government agencies healthcare information are in danger.
How could this new thrust of computing strength give us new tiers of power to analyze IT systems at a more granular level for security vulnerabilities and protect us through more complex layers of quantum cryptography?
The document discusses attention mechanisms for encoder-decoder neural networks. It describes traditional encoder-decoder models that compress all input information into a fixed vector, which cannot encode long sentences. Attention mechanisms allow the decoder to access the entire encoded input sequence and assign weights to input elements based on their relevance to predicting the output. The core attention model uses an alignment function to calculate energy scores between the input and output, a distribution function to calculate attention weights from the energy scores, and a weighted sum to compute the context vector used by the decoder. Various alignment functions are discussed, including dot product, additive, and deep attention.
This document discusses the YOLO object detection algorithm and its applications in real-time object detection. YOLO frames object detection as a regression problem to predict bounding boxes and class probabilities in one pass. It can process images at 30 FPS. The document compares YOLO versions 1-3 and their improvements in small object detection, resolution, and generalization. It describes implementing YOLO with OpenCV and its use in self-driving cars due to its speed and contextual awareness.
Each grain must hold a charge
When their volume becomes too little, they will no longer be stable & will be influenced by ambient thermal energy
With current technology, this will happen around 130 Gb/in2
Understanding Black Box Models with Shapley ValuesJonathan Bechtel
This document provides an overview of SHAP (SHapley Additive exPlanations), a game theory-based method for explaining the output of any machine learning model. It describes how SHAP values quantify the contribution of each feature towards a model's prediction using a technique called Shapley sampling. The document discusses how SHAP addresses limitations of other interpretability methods, and how it can be used to analyze feature interactions, classify images with CNNs, and provide explanations for different model types like trees and deep learning models. It positions SHAP as a widely adopted tool for making machine learning models more interpretable and understandable.
The document proposes a scalable AI accelerator ASIC platform for edge AI processing. It describes a high-level architecture based on a scalable AI compute fabric that allows for fast learning and inference. The architecture is flexible and can scale from single-chip solutions to multi-chip solutions connected via high-speed interfaces. It also provides details on the AI compute fabric, processing elements, and how the platform could enable high-performance edge AI processing.
Introduction to Artificial Neural Networks - PART I.pdfSasiKala592103
The document provides an introduction to artificial neural networks and machine learning. It discusses how machine learning mimics human learning by observing data, identifying patterns, and building models to explain the patterns. The document uses the example of recognizing the pattern of day and night to illustrate how humans learn by observing a phenomena, identifying a relationship between entities, and selecting the model that best fits observations. It explains that machine learning follows a similar process to perform tasks like pattern recognition, decision making, and optimization by analyzing large amounts of data using algorithms to identify patterns.
Introduction to Artificial Neural Networks - PART IV.pdfSasiKala592103
This document discusses shallow neural networks and multi-layer perceptrons. It defines shallow neural networks as having one hidden layer, while deep neural networks have many hidden layers. Multi-layer perceptrons are introduced as a type of shallow neural network that can solve problems like the XOR problem using a hidden layer. The backpropagation algorithm is described as a way to train multi-layer perceptrons by propagating error backwards from the output to update weights between layers. An example is provided to demonstrate how forward and backward passes are used in backpropagation to train a multi-layer perceptron.
DetectoRS for Object Detection/Segmentation
On COCO test-dev, DetectoRS achieves state-of-the art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation.
(2020.07)
Despite the increase of deep learning practitioners and researchers, many of them do not use GPUs, this may lead to long training/evaluation cycles and non-practical research.
In his talk, Lior shares how to get started with GPUs and some of the best practices that helped him during research and work. The talk is for everyone who works with machine learning (deep learning experience is NOT mandatory!), It covers the very basics of how GPU works, CUDA drivers, IDE configuration, training, inference, and multi-GPU training.
PhD Oral Defense of Md Kafiul Islam on "ARTIFACT CHARACTERIZATION, DETECTION ...Md Kafiul Islam
This document summarizes an oral defense presentation for a PhD dissertation on artifact characterization, detection, and removal from neural signals. The presentation outlines the background on in-vivo neural signals and EEG, problems and motivation regarding artifacts corrupting signals, thesis objectives, literature review on existing artifact removal methods, contributions of the dissertation including artifact study and proposed removal algorithms, and plans for future work. The presentation aims to investigate artifacts in neural data, develop automated detection and removal without distorting signals, evaluate methods, and improve applications like epilepsy detection and brain-computer interfaces.
The document summarizes the U-Net convolutional network architecture for biomedical image segmentation. U-Net improves on Fully Convolutional Networks (FCNs) by introducing a U-shaped architecture with skip connections between contracting and expansive paths. This allows contextual information from the contracting path to be combined with localization information from the expansive path, improving segmentation of biomedical images which often have objects at multiple scales. The U-Net architecture has been shown to perform well even with limited training data due to its ability to make use of context.
This document is a slide presentation on recent advances in deep learning. It discusses self-supervised learning, which involves using unlabeled data to learn representations by predicting structural information within the data. The presentation covers pretext tasks, invariance-based approaches, and generation-based approaches for self-supervised learning in computer vision and natural language processing. It provides examples of specific self-supervised methods like predicting image rotations, clustering representations to generate pseudo-labels, and masked language modeling.
The document summarizes a presentation on quantum information and technologies. It discusses:
1) How quantum computing could enable solving problems in fields like space science, biology, and finance faster than classical computers by taking advantage of quantum properties like superposition and entanglement.
2) Some of the basic concepts in quantum information like qubits, qudits, wavefunctions, error correction, and different methods for building quantum computers like superconducting and optical approaches.
3) The status of quantum computing including cloud access to quantum processors with over 100 qubits now available from IBM, though fully error corrected quantum computers still remain in development.
Erie Insurance Group interview questions and answersleybrad610
This document provides interview preparation materials for Erie Insurance Group, including answers to common interview questions, tips for researching the company, and additional job interview resources. Sample answers are given for questions like "What is your greatest weakness?" and "Why should we hire you?". The document emphasizes being honest, relating your strengths to the role, and asking the interviewer questions about the company and position. Additional links provide practice interview questions and a free ebook with sample answers.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
Slides from the UPC reading group on computer vision about the following paper:
Redmon, Joseph, Santosh Divvala, Ross Girshick, and Ali Farhadi. "You only look once: Unified, real-time object detection." arXiv preprint arXiv:1506.02640 (2015).
This document outlines a talk on high-performance computing trends. It discusses the need for HPC, parallel architectures and systems, high-throughput computing, distributed computing models like grids and clouds, and examples like the Top500 supercomputers and Amazon Web Services. It also previews a question and answer session at the end.
Cyber Security and Post Quantum Cryptography By: Professor Lili SaghafiProfessor Lili Saghafi
Quantum computing has the potential to transform cybersecurity.
Some encryption algorithms are thought to be unbreakable, except by brute-force attacks.
Although brute-force attacks may be hard for classical computers, they would be easy for quantum computers making them susceptible to such attacks.
All financial institutions, government agencies healthcare information are in danger.
How could this new thrust of computing strength give us new tiers of power to analyze IT systems at a more granular level for security vulnerabilities and protect us through more complex layers of quantum cryptography?
The document discusses attention mechanisms for encoder-decoder neural networks. It describes traditional encoder-decoder models that compress all input information into a fixed vector, which cannot encode long sentences. Attention mechanisms allow the decoder to access the entire encoded input sequence and assign weights to input elements based on their relevance to predicting the output. The core attention model uses an alignment function to calculate energy scores between the input and output, a distribution function to calculate attention weights from the energy scores, and a weighted sum to compute the context vector used by the decoder. Various alignment functions are discussed, including dot product, additive, and deep attention.
This document discusses the YOLO object detection algorithm and its applications in real-time object detection. YOLO frames object detection as a regression problem to predict bounding boxes and class probabilities in one pass. It can process images at 30 FPS. The document compares YOLO versions 1-3 and their improvements in small object detection, resolution, and generalization. It describes implementing YOLO with OpenCV and its use in self-driving cars due to its speed and contextual awareness.
Each grain must hold a charge
When their volume becomes too little, they will no longer be stable & will be influenced by ambient thermal energy
With current technology, this will happen around 130 Gb/in2
Understanding Black Box Models with Shapley ValuesJonathan Bechtel
This document provides an overview of SHAP (SHapley Additive exPlanations), a game theory-based method for explaining the output of any machine learning model. It describes how SHAP values quantify the contribution of each feature towards a model's prediction using a technique called Shapley sampling. The document discusses how SHAP addresses limitations of other interpretability methods, and how it can be used to analyze feature interactions, classify images with CNNs, and provide explanations for different model types like trees and deep learning models. It positions SHAP as a widely adopted tool for making machine learning models more interpretable and understandable.
The document proposes a scalable AI accelerator ASIC platform for edge AI processing. It describes a high-level architecture based on a scalable AI compute fabric that allows for fast learning and inference. The architecture is flexible and can scale from single-chip solutions to multi-chip solutions connected via high-speed interfaces. It also provides details on the AI compute fabric, processing elements, and how the platform could enable high-performance edge AI processing.
Introduction to Artificial Neural Networks - PART I.pdfSasiKala592103
The document provides an introduction to artificial neural networks and machine learning. It discusses how machine learning mimics human learning by observing data, identifying patterns, and building models to explain the patterns. The document uses the example of recognizing the pattern of day and night to illustrate how humans learn by observing a phenomena, identifying a relationship between entities, and selecting the model that best fits observations. It explains that machine learning follows a similar process to perform tasks like pattern recognition, decision making, and optimization by analyzing large amounts of data using algorithms to identify patterns.
Introduction to Artificial Neural Networks - PART IV.pdfSasiKala592103
This document discusses shallow neural networks and multi-layer perceptrons. It defines shallow neural networks as having one hidden layer, while deep neural networks have many hidden layers. Multi-layer perceptrons are introduced as a type of shallow neural network that can solve problems like the XOR problem using a hidden layer. The backpropagation algorithm is described as a way to train multi-layer perceptrons by propagating error backwards from the output to update weights between layers. An example is provided to demonstrate how forward and backward passes are used in backpropagation to train a multi-layer perceptron.
Introduction to Artificial Neural Networks - PART II.pdfSasiKala592103
The document discusses artificial neural networks (ANNs), comparing their structure and functions to biological neural networks. It describes how ANNs are modeled after the human brain by using artificial neurons that accumulate input information, pass information through a threshold or activation function, and store knowledge in weighted connections. The key components of artificial neurons like inputs, weights, summation, and output are analogous to biological neurons' dendrites, synapses, soma, and axon. ANNs can learn and operate in parallel like the brain, though they are less fault tolerant. The document outlines feedforward and feedback ANN architectures, learning algorithms, activation functions, and classifications of ANNs.
This document discusses object identification using convolutional neural networks and the YOLO detection algorithm. It begins with an introduction to neural networks and their history. It then discusses datasets used to train object detection models. The document describes experiments conducted using the YOLO detector on different sized images to evaluate performance. Processing speed and objects detected were compared between the CPU and GPU. The YOLO detector was then tested on a set of 500 images, and its performance metrics were reported.
Gridforum David De Roure Newe Science 20080402vrij
The document discusses the evolution of e-Science and how it enables new forms of collaborative research. Key points include:
- e-Science has progressed from specialized teams doing "heroic science" to everyday researchers conducting routine research using ubiquitous digital tools and data sharing.
- Web 2.0 technologies and approaches like open data, workflows, and social networking are empowering researchers and supporting new types of collaborative, data-driven science.
- Future e-Science relies on making these technologies simple and accessible to researchers from all domains to further break down barriers to collaborative, data-centric research.
This document provides details of an industrial training presentation on artificial intelligence, machine learning, and deep learning that was delivered at the Centre for Advanced Studies in Lucknow, India from July 15th to August 14th, 2020. The presentation covered theoretical background on AI, machine learning, and deep learning. It was divided into 4 modules that discussed topics such as what machine learning is, supervised vs unsupervised learning, classification vs clustering, neural networks, activation functions, and applications of deep learning. The conclusion discussed how AI is impacting many industries and emerging technologies and will continue to be a driver of innovation.
This document provides an overview of applications of fuzzy logic in neural networks. It discusses fuzzy neurons as a combination of fuzzy logic and neural networks where the neuron's activation function is replaced with a fuzzy logic operation. Different types of fuzzy neurons are described, including OR, AND, and OR/AND fuzzy neurons. Supervised learning in fuzzy neural networks is also covered. The document concludes with advantages of fuzzy logic systems over traditional neural networks, such as the ability of fuzzy systems to systematically include linguistic knowledge.
Introduction to the Artificial Intelligence and Computer Vision revolutionDarian Frajberg
Deep learning and computer vision have revolutionized artificial intelligence. Deep learning uses artificial neural networks inspired by the human brain to learn from large amounts of data without being explicitly programmed. Computer vision gives computers the ability to understand digital images and videos. Key breakthroughs include AlexNet achieving unprecedented accuracy on ImageNet in 2012, demonstrating the power of deep convolutional neural networks for computer vision tasks. Challenges remain around ensuring AI systems are beneficial to society, avoiding data biases, and increasing transparency.
This document discusses building 3rd generation AI inspired by insect brains. Researchers at the University of Sussex are working on projects to build smarter robots by modeling the brain and learning abilities of bees. The projects combine neuroscience, robotics, and AI to decipher the "brain algorithms" of insects like bees and ants. They are using neural simulations, novel lightweight robots, and machine learning on specialized hardware like GPUs. The goal is to understand how small insect brains can efficiently navigate and learn routes despite having few neurons and low visual resolution. Researchers hope to learn tricks from insects to build AI that learns routes through familiarity rather than precise recognition. They are testing models where neural networks learn to associate views with actions instead of locations.
Object recognition with cortex like mechanisms pami-07dingggthu
This document summarizes a new framework for robust object recognition inspired by the visual cortex. It describes a hierarchical system that builds an increasingly complex and invariant feature representation through alternating template matching and maximum pooling operations. The approach demonstrates strong performance on single object recognition, multiclass categorization, and scene understanding tasks. Given its biological constraints, it performs surprisingly well and competes with state-of-the-art systems while learning from few examples. The success of this cortex-like model provides plausibility for feedforward models of object recognition in the visual cortex.
SURVEY ON BRAIN – MACHINE INTERRELATIVE LEARNINGIRJET Journal
1) The document discusses the relationship between brain-machine learning and how artificial neural networks mimic the human brain. It explores how the brain learns tasks unconsciously over time through automated algorithms and how neural networks similarly learn through machine learning.
2) Key aspects of artificial neural networks like perceptrons are explained through mathematical equations. Perceptrons take in inputs, assign weights, and use threshold functions to determine outputs similar to biological neurons.
3) The relationship between artificial intelligence, machine learning, and neuroscience is interdependent. AI helps further understand the brain through modeling, while the brain's learning inspires new machine learning techniques. Both aim to automate tasks and recognize patterns from data.
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.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed 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 image captioning.
Recurrent Neural Networks (RNNs) represent the reference class of Deep Learning models for learning from sequential data. Despite the widespread success, a major downside of RNNs and commonly derived ‘gating’ variants (LSTM, GRU) is given by the high cost of the involved training algorithms. In this context, an increasingly popular alternative is the Reservoir Computing (RC) approach, which enables limiting the training algorithm to operate only on a restricted set of (output) parameters. RC is appealing for several reasons, including the amenability of being implemented in low-powerful edge devices, enabling adaptation and personalization in IoT and cyber-physical systems applications.
This webinar will introduce Reservoir Computing from scratch, covering all the fundamental design topics as well as good practices. It is targeted to both researchers and practitioners that are interested in setting up fastly-trained Deep Learning models for sequential data.
This document describes a student project to develop a real-time eye gaze tracking system using a low-cost webcam. It provides background on eye tracking research and methods, including a brief history and modern techniques like video-based and invasive methods. It then discusses the general concepts and tools used in the project, including the structure of the human eye, Purkinje images, infrared light, webcams, and MATLAB. The implementation, applications, results and future improvements are also summarized.
NEURAL NETWORK FOR THE RELIABILITY ANALYSIS OF A SERIES - PARALLEL SYSTEM SUB...IAEME Publication
Artificial neural networks can achieve high computation rates by employing a massive number of simple processing elements with a high degree of connectivity between the elements. Neural networks with feedback connections provide a computing model capable of exploiting fine- grained parallelism to solve a rich class of complex problems. In this paper we discuss a complex series-parallel system subjected to finite common cause and finite human error failures and its reliability using neural network method.
The document is a dissertation submitted to Gujarat University in partial fulfillment of a Master's degree in Computer Application, which discusses character recognition using neural networks. It provides an index of the contents including the introduction to neural networks, their architecture and applications, an introduction to character recognition, the use of Matlab and its neural network toolbox, a literature survey, the proposed work on digit recognition, potential enhancements, and conclusions. The dissertation was submitted by Sachinkumar M. Bharadva and Dhara Solanki under the guidance of their internal guide Mr. Sandeep R. Vasant at the AES Institute of Computer Studies.
Nero-IR is a novel area of research under cognitive psychology, neuro-physiological methods (eye tracking, EEG, EOG, and GSR) and machine learning to understand information searchers and to improve search experience. Neuro-IR is useful in investigating the search as a learning process and to employ these sensory data as assessment of reading, mind-wandering and in inferring metadata features for machine learning models. In this talk, I will introduce a unification framework for neuro-physiological data; practically these models provide context for user interactions. I will show how we can take advantage of many existing interactions combining various sensory platforms (e.g., PupilLabs, Emotiv, Empatica E4). Information fusion can provide numerous benefits in combining multiple-sources of neuro-physiological data. The most obvious among them is the expected performance gain due to combination of evidence from multiple cues. As a practical matter, acquisition of physiological metadata is a research frontier.
An Overview On Neural Network And Its ApplicationSherri Cost
Neural networks are computational models that can learn from large amounts of data to find patterns and make predictions. They are inspired by biological neural networks in the brain. The document provides an overview of how artificial neural networks function by organizing layers of nodes that are trained to process input data. It also discusses applications of neural networks such as classification, prediction, clustering, and associating patterns. Neural networks are well-suited for analyzing big data due to their ability to handle ambiguous or incomplete information.
Similar to SSK_Artificial Neural Networks Basic to Models.pdf (20)
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Generative AI Use cases applications solutions and implementation.pdfmahaffeycheryld
Generative AI solutions encompass a range of capabilities from content creation to complex problem-solving across industries. Implementing generative AI involves identifying specific business needs, developing tailored AI models using techniques like GANs and VAEs, and integrating these models into existing workflows. Data quality and continuous model refinement are crucial for effective implementation. Businesses must also consider ethical implications and ensure transparency in AI decision-making. Generative AI's implementation aims to enhance efficiency, creativity, and innovation by leveraging autonomous generation and sophisticated learning algorithms to meet diverse business challenges.
https://www.leewayhertz.com/generative-ai-use-cases-and-applications/
Accident detection system project report.pdfKamal Acharya
The Rapid growth of technology and infrastructure has made our lives easier. The
advent of technology has also increased the traffic hazards and the road accidents take place
frequently which causes huge loss of life and property because of the poor emergency facilities.
Many lives could have been saved if emergency service could get accident information and
reach in time. Our project will provide an optimum solution to this draw back. A piezo electric
sensor can be used as a crash or rollover detector of the vehicle during and after a crash. With
signals from a piezo electric sensor, a severe accident can be recognized. According to this
project when a vehicle meets with an accident immediately piezo electric sensor will detect the
signal or if a car rolls over. Then with the help of GSM module and GPS module, the location
will be sent to the emergency contact. Then after conforming the location necessary action will
be taken. If the person meets with a small accident or if there is no serious threat to anyone’s
life, then the alert message can be terminated by the driver by a switch provided in order to
avoid wasting the valuable time of the medical rescue team.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
This document provides basic guidelines for imparitallity requirement of ISO 17025. It defines in detial how it is met and wiudhwdih jdhsjdhwudjwkdbjwkdddddddddddkkkkkkkkkkkkkkkkkkkkkkkwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwwioiiiiiiiiiiiii uwwwwwwwwwwwwwwwwhe wiqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq gbbbbbbbbbbbbb owdjjjjjjjjjjjjjjjjjjjj widhi owqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq uwdhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhwqiiiiiiiiiiiiiiiiiiiiiiiiiiiiw0pooooojjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjjj whhhhhhhhhhh wheeeeeeee wihieiiiiii wihe
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Road construction is not as easy as it seems to be, it includes various steps and it starts with its designing and
structure including the traffic volume consideration. Then base layer is done by bulldozers and levelers and after
base surface coating has to be done. For giving road a smooth surface with flexibility, Asphalt concrete is used.
Asphalt requires an aggregate sub base material layer, and then a base layer to be put into first place. Asphalt road
construction is formulated to support the heavy traffic load and climatic conditions. It is 100% recyclable and
saving non renewable natural resources.
With the advancement of technology, Asphalt technology gives assurance about the good drainage system and with
skid resistance it can be used where safety is necessary such as outsidethe schools.
The largest use of Asphalt is for making asphalt concrete for road surfaces. It is widely used in airports around the
world due to the sturdiness and ability to be repaired quickly, it is widely used for runways dedicated to aircraft
landing and taking off. Asphalt is normally stored and transported at 150’C or 300’F temperature
DESIGN AND MANUFACTURE OF CEILING BOARD USING SAWDUST AND WASTE CARTON MATERI...
SSK_Artificial Neural Networks Basic to Models.pdf
1. AN INTRODUCTION TO
ARTIFICIAL NEURAL NETWORKS
Dr.S.SASIKALA
Department of ECE
Kumaraguru College of Technology
Coimbatore
Department of
Electronics and Communication Engineering
Since 1986
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3. What is Learning?
Change is The Result of all True Learning
Leo Buscaglia
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4. What is Learning?
• Learning happens when you observe a
phenomena and recognize a pattern.
• You try to understand this pattern by finding
out if there is any relationship between
the entities involved in that phenomena.
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5. What is Learning?
• Take the example of a simple phenomenon
that we observe daily — the occurrence of day
and night – How do you realize?
Is there a pattern? Yes
Day time: A fixed time period, we
are exposed to light and heat of
the sun.
Night time: Another fixed period,
we are deprived of light and heat
from the sun.
This pattern repeats over and
over and over
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6. What is Learning?
• how this pattern occurs?
• There are 2 entities involved in this observation
— Sun and Earth.
• Is there a relationship between the amount of light(and
heat) originating from the sun and the surface of earth
receiving it.
• The pattern suggests that the surface of the earth
receives the light alternatively
— gets it during the daytime
— does not get it during night-time.
• How is this possible?
— There are many possibilities
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7. What is Learning?
• There are 3 conclusions derived called
“models” that explain the observed
phenomena.
• Model 1: Day/Night is a function of Magical
ON/OFF switch of sun
• Model 2: Day/Night is a function of the
Revolution of Sun around the earth
• Model 3: Day/Night is a function of Rotation of
Earth on its axis
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8. What is Learning?
• The question now arises
— Which model(or function) is more accurate?
As per the observations/findings of different
philosophers/scientists across the ages, Model
3 is the most accurate model which explains
the phenomena of Day and Night.
— We can say, that this model “fits” best for
the observations around this phenomena.
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9. What is Learning?
• Once a model has been built, it can be used
to predict future outcomes for that
phenomena.
• In our example, our model can safely predict
that occurrence of day/night will continue to
happen until, for some reason, the earth stops
rotating or sun runs out of its energy
➢ Will the earth stop rotating?
➢ When will the sun spent all of its energy ?
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10. This is How Humans Learn
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11. Human Learning
• Observing something, identifying a pattern,
building a theory (model) to explain this
pattern and testing this theory to check
whether it fits in most or all observations.
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12. How Human Learn?
Parents Parents
Siblings
Teachers
Parents
Siblings
Teachers
Friends
Parents
Siblings
Teachers
Friends
Society
Experience
Parents
Siblings
Wife
Friends
Society
Colleagues
Parents
Siblings
Wife
Children
Friends
Society
Colleagues
Parents
Siblings
Wife
Children
Grand
Children
Friends
Society
Colleagues
BOOKS BOOKS
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13. Is it possible for a machine to mimic
the process of human learning?
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14. Human vs Machine
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15. Machine Can Mimic
Human Learning Process
• The basic idea remains the same
• As with humans, machines are fed with
observations (data)
• The learning algorithm try to find out a
pattern among the data which best fits the
observations
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16. Human learning vs Machine Learning
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17. Machine Learning
A very powerful extension of
Human Brainpower
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18. Task of Machine Learning
• Pattern Recognition
• Decision Making
• Optimization
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19. Pattern Recognition
A pattern
• is an object, process or event that can be
given a name
• can either be seen physically or it can be
observed
• Eg. Eye colour, finger prints, handwriting
Recognition
• process of identifying the patterns
Pattern recognition
• is identifying patterns in data
• Process of converting the raw data into a
form that is amenable for a machine to use
• Pattern recognition involves classification
and cluster of patterns.
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21. August 27, 2022
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22. Pattern Recognition
• Humans
Can perceive pattern naturally
But more computational time is required
• Machines
Computational speed is very high compared to humans.
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23. Human - Very Good in PR
Humans have
Ability to learn from
experience
Brain with lot of information
processing cells
About 1011 neurons
interconnected to form a vast
and complex network like
structure
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24. BIOLOGICAL AND ARTIFICIAL
NEURAL NETWORKS
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25. Biological Neuron
Cell body(Soma)
• Containing organelles of the neuron
Dentrites (Rx)
• Tree-like structure originating to cell body that
receives the signal from surrounding neurons
Axon (TX)
• Long connection extending from cell body and carries signal
• There is only one axon per neuron that axon may divide in many branches at its end
and connected to other cells to transmits the signal from one neuron to others
Synapse
• Small-bulb like organ neuron at the end of axon which introduces the signal to the
near by dendrites of the other through chemical diffusion
Neuron
• Summed up all the inputs and process the sum by a threshold function and
produces an output signal.
• A neuron fires an electrical impulse only if certain condition is met
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26. Biological Neural Network
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27. How do you model an Artificial Neuron
By simulating functioning of a biological neuron
❑Function 1 – Accumulation of Information
Summation or Net Input Calculation
❑Function 2 – Passing of Information
Threshold or Activation or Producing output
Simulation involves
❑Identify the equivalent mathematical operator for the function
❑Design a mathematical model that process information
Artificial Neuron Resembles the human brain in two respects:
❑Knowledge acquisition through learning
❑Storage of knowledge in the synaptic weights
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28. Biological Neuron and Artificial Neuron
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29. Biological & Artificial Neuron
Resemblance
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30. ANN vs BNN
BNN ANN
Soma Node
Dendrites Input
Synapse Weights or Interconnections
Axon Output
Massively parallel, slow but superior than
ANN
Massively parallel, fast but inferior than BNN
10
11
neurons and 10
15
interconnections 10
2
to 10
4
nodes mainly depends on the type
of application and network designer
They can tolerate ambiguity Very precise, structured and formatted data
is required to tolerate ambiguity
Performance degrades with even partial
damage
It is capable of robust performance, hence
has the potential to be fault tolerant
Stores the information in the synapse Stores the information in continuous
memory locations
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31. ANN - Function
-
f
Weighted
sum
Input
vector x
Output y
Weight
vector
w
w0j
w1j
wnj
x0
x1
xn
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32. What is ANN
Artificial Neuron
➢A digital construct that seeks to simulate the behavior of a
biological neuron in the brain.
➢They may be physical devices, or purely mathematical
constructs.
Artificial Neural Networks (ANN)
➢ Networks of Artificial Neurons
➢A parallel computational system consisting of a huge number
of simple and massively connected processing elements
connected together in a specific manner in order to perform a
particular task
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33. History of ANN
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34. Model of Artificial Neural Network
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35. Model of Artificial Neural Network
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• In the general model of ANN, the net input is
calculated by using the equation
• The output can be calculated by applying the
activation function over the net input
36. ANN - Building Blocks
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37. CLASSIFICATIONS OF ANN
• Based on the architecture
➢Feed Forward Neural Network (FFNN)
➢Feed Back Neural Network (FBNN)
➢Recurrent Neural Network (RNN)
➢Competitive Neural Network (CNN)
• Based on the learning algorithm
➢Supervised Learning
➢Unsupervised Learning
➢Reinforcement Learning
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38. Activation Functions
➢Activation functions are mathematical equations i.e a non-linear
transformations attached to each neuron in the network, which
determines whether the neuron should be activated (“fired”)
or not by calculating weighted sum and further adding bias with
it.
➢The purpose of the activation function is to introduce non-
linearity into the output of a neuron.
➢Activation functions also help normalize the output of each
neuron to a range between 1 and 0 or between -1 and 1.
➢The activation function does the non-linear transformation to
the input making it capable to learn and perform more complex
tasks.
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39. Activation Functions
Linear Activation Function or identity function
Sigmoid Activation Function
➢Binary sigmoidal function
➢Bipolar sigmoidal function
F(x) = 1 if x > 0 else 0 if x < 0
Binary Step Activation Function
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40. ANN MODELS
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41. ANN Models
Models
➢McCulloch and Pitts Neuron
➢Hebb Network
➢Perceptron Network
➢Linear Separability
Insight
➢Architecture
➢Net Input Calculation
➢Output Calculation
➢Weight Updation - Learning
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42. McCulloch and Pitts Neuron
➢ Activation is a binary step function
➢ Widely used in designing logic function
without bias
with bias
yin
= b + xi
wi
➢ Usually called as M-P Neuron or Threshold Logic Unit / gate
➢ Simply classifies the set of inputs into two different classes.
➢ Bias b is used to adjust the output along with the weighted
sum of the inputs to the neuron.
➢ b is a constant helps the model in a way
that it can fit best for the given data.
➢ Net input is calculated as
yin
= xi
wi
f(x) = 1 if x > 0 else 0 if x < 0
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43. Hand Worked Example-MP Neuron
Calculation of net input without bias
[x1,x2,x3] = [0.1, 0.6, 0.3]
[w1,w2,w3] = [0.3 ,0.2, -0.4]
yin= xwT
yin=x1w1 + x2w2 + x3w3
= 0.1*0.3 + 0.6*0.2 + 0.3*(-0.4)
= 0.03 + 0.12 – 0.12
= 0.03
X1=0.1
X2=0.6
X3=0.3
w1=0.3
w2=0.2
w3=0.4
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Calculation of output using binary step activation function
y = F(yin) = 1
44. Hand Worked Example-MP Neuron
Calculation of output using binary sigmoidal function
X = [x1,x2,x3] = [0.1, 0.6, 0.3]
W = [w1,w2,w3] = [0.3, 0.2,-0.4]
yin= b +xwT
Assuming x0 = 1 and w0=b
X = [x0, x1,x2,x3]
W = [w0,w1,w2,w3]
yin= xwT
yin=x1w1 +x1w1 + x2w2 + x3w3
= 1*0.5 + 0.1*0.3 + 0.6*0.2 + 0.3*(-0.4)
= 0.5 + 0.03 + 0.12 – 0.12
= 0.53
X1=0.1
X2=0.6
X3=0.3
w1=0.3
w2=0.2
w3=-0.4
b=0.5
1
y = f(yin) = 1 using Binary Step
= 0.63 using binary
sigmoid
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45. Implementation of AND function
x1
x2
w1
w2
X1 X2 y
1 1 1
1 0 0
0 1 0
0 0 0
Assume Initial Weights w1 and w2 = 1
For inputs
➢ (1,1)→ yin=x1w1+x2w2 = 2
➢ (1,0) → 1
➢ (0,1) → 1
➢ (0,0) → 0
➢Assume threshold value Ѳ = 2
0if yin 2
y =f (yin)=
1if yin 2
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46. Implementation of OR function
x1 x2 Y
1 1 1
1 0 1
0 1 1
0 0 0
Assume Initial Weights w1 and
w2 = 1 & b=0.5
For inputs
➢ (1,1)→ yin=x1w1+x2w2 + b= 2.5
➢ (1,0) → 1.5
➢ (0,1) → 1.5
➢ (0,0) → 0
➢Assume threshold value Ѳ = 1.5
y =f (yin)=
1if yin 1.5
0if yin 1.5
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47. Hebb Network
➢ Observed that the learning in human brain takes place by
the change in synaptic gap.
➢ Weight vector is found to increase proportionately to the
product of input and output.
wi
(new )=wi
(old )+xi
y
b(new )=b (old )+y
➢ Weight and bias adjustment
➢ Change in weight w =xi
y
➢ Activation function is identity function f (yin ) = yin
➢ More suited for bipolar data
➢ Used for Pattern association, classification and clustering
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48. Training Steps
1.Initially, the weights are set to zero, i.e. w =0 for all inputs
i =1 to n and n is the total number of input neurons.
2.The activation function for inputs is generally set as an
identity function.
3.The activation function for output is also set to y= t.
4.The weight adjustments and bias are adjusted to:
5.The steps 2 to 4 are repeated for each input vector and
output.
wi
(new )=wi
(old )+xi
y
b(new )=b (old )+y
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49. Implementation of AND function
X1 X2 y
1 1 1
1 0 0
0 1 0
0 0 0
Training data →truth table of AND function
In bipolar form 1 →1 & 0 → -1
x1
x2
w1
w2
x0
b
➢ Initially the weights are set to zero w1=w2=b=0
➢ Present the first set inputs and apply Hebb rule
[x1 x2 x0] = [1 1 1] and y=[1]
wi(new)=wi(old) + xiy
• w1(new) = w1(old)+x1y → 0 + 1 *1 = 1
• w2(new)=w2(old)+x2y → 0 + 1 * 1 = 1
• b(new) = b(old) + y → 0 + 1 = 1
➢ Change in weight
• ∆wi=xiy
• ∆w1=x1y → 1 * 1 = 1
• ∆ w2 = x2y → 1 * 1 = 1
• ∆b=y = 1
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50. Implementation of AND function
x1
x2
2
2
x0
-2
➢ Present the second set inputs and apply Hebb rule
– [x1 x2 x0] = [1 -1 1] and y=[-1]
– wi(new)=wi(old) + xiy
• w1(new) = w1(old)+x1y → 1 + 1 *-1 =0
• w2(new)=w2(old)+x2y → 1 + -1 * -1 = 2
• b(new) = b(old) + y → 1 + -1 = 0
➢ Change in weight
– ∆wi=xiy
• ∆w1=x1y → 1 * -1 =-1
• ∆ w2 = x2y → -1 * -1 = 1
• ∆b=y = -1
X1 X2 X0 Y ∆w1 ∆ w2 ∆b W1
(0)
W2
(0)
B
(0)
1 1 1 1 1 1 1 1 1 1
1 -1 1 -1 -1 1 -1 0 2 0
-1 1 1 -1 1 -1 -1 1 1 -1
-1 -1 1 -1
Dr
.P
.Ganes
1
hKumar,
1
Annauniver
-1
sity
2 2 -2
Hebb Net for AND Function
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51. Perceptron Network
➢Perceptron Networks are single-layer feed-forward
networks introduced by Rosenblatt.
➢The Perceptron consists of an input layer, a hidden layer,
and output layer.
➢The input layer is connected to the hidden layer through
weights which may be inhibitory or excitatory or zero (-
1, +1 or 0).
➢The activation function used is a binary step function for
the input layer and the hidden layer.
➢The output is Y= f (y)
➢The activation function is: F(y)=
1, if y ≥ θ
0, if - θ ≤ y ≤ θ
-1, if y ≤ - θ
where θ is threshold
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52. Perceptron Learning Rule
➢ The weight updation takes place between the hidden layer
and the output layer to match the target output.
➢ The error is calculated based on the actual output and the
desired output.
➢ If the output matches the target then no weight updation
takes place.
➢ The weights in the network can be set to any values initially.
➢ The Perceptron learning will converge to weight vector that
gives correct output for all input training pattern and this
learning happens in a finite number of steps.
➢ The Perceptron rule can be used for both binary and bipolar
inputs.
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53. Training Steps
➢ Let there be “n” training input vectors and x(n) and t(n) are
associated with the target values.
➢ Initialize the weights and bias to zero for easy calculation
and the learning rate be 1.
➢ The input layer has identity activation function so x(i)= y(i).
➢ To calculate the output of the network:
•Calculate the net input to the output neuron
•Apply the activation function over the net input
➢ Now based on the output y, compare the desired target
value (t) and the actual output.
➢ Update Weights and bias if y t.
➢ Continue the iteration until there is
no weight change. Stop once this
condition is achieved
Weight Updation
if output (Y) t arget (t),
then w (new ) =w (old ) +tx
b(new ) =b(old ) +t
else w (new ) =w (old )
b(new ) =b(old )
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54. Inputs Bias Target Net i/p O/p Weight Changes New Weights
w1 w2 b t yin y w1 w2 b W1 w2 b
Implementation of AND function
The EPOCHS are the cycle of input patterns fed to the system until there is no weight change
required and the iteration stops.
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55. Linear Separability
• Linear Separability is possible by ANN(with input and output nodes alone)
only when the given problem is linear otherwise it is not possible.
• But Most of the real world problems are non linear in nature.
• Non-linear problems can be easily solved by introducing one or more
hidden layers between the input and output layers
x2
x1
x2
After Trained by NeuralNetwork
• Concept of separating the input data into classes by means of
straight line called decision line or decision making line or decision
support line or linearly separable line.
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56. Linear Separability Illustrative Example
X1 X2 Y
0 0 0
0 1 1
1 0 1
1 1 1
‘OR’ GATE
X1
X2
‘AND’ GATE
X1 X2 Y
0 0 0
0 1 0
1 0 0
1 1 1
X1
X1
X2
X2
‘OR’ gate and ‘AND’ gate are LINEARLYSEPARABLE
‘XOR’ GATE
X1 X2 Y
0 0 1
0 1 0
1 0 0
1 1 1
‘XOR’ gate is NON-LINEAR
Logic 1 O/p
Logic 0 O/p
NOTE: Most of the data of real world problems are non linear only
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58. Shallow Neural Networks
• A neural network with one hidden layer is considered
a shallow neural network whereas a network with
many hidden layers and a large number of neurons in
each layer is considered a deep neural network.
• A “shallow” neural network has only three layers of
neurons:
➢An input layer that accepts the independent
variables or inputs of the model
➢One hidden layer
➢An output layer that generates predictions
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59. Shallow Machine Learning
• The features extraction in Shallow Machine
Learning is a manual process that requires
domain knowledge of the data that we
are learning from.
• In other words, "Shallow Learning" is a type
of machine learning where we learn from
data described by pre-defined features.
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60. Shallow Neural Networks
• Multilayer Perceptron Network (MLPN)
• Radial Basis Function Network (RBFN)
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62. We will introduce the MLP and the backpropagation
algorithm which is used to train it
MLP used to describe any general feedforward (no
recurrent connections) network
However, we will concentrate on nets with units
arranged in layers
x1
xn
62
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63. Different books refer to the above as either 4 layer (no. of
layers of neurons) or 3 layer (no. of layers of adaptive
weights). We will follow the latter convention
1st question:
what do the extra layers gain you? Start with looking at
what a single layer can’t do
x1
xn
63
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64. Perceptron Learning Theorem
• Recap: A perceptron (threshold unit) can
learn anything that it can represent (i.e.
anything separable with a hyperplane)
64
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65. The Exclusive OR problem
A Perceptron cannot represent Exclusive OR
since it is not linearly separable.
65
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66. 66
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67. Minsky & Papert (1969) offered solution to XOR problem by
combining perceptron unit responses using a second layer of
Units. Piecewise linear classification using an MLP with
threshold (perceptron) units
1
2
+1
+1
3
67
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69. Properties of architecture
• No connections within a layer
y f w x b
i ij j i
j
m
= +
=
( )
1
Each unit is a perceptron
69
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70. Properties of architecture
• No connections within a layer
• No direct connections between input and output layers
•
y f w x b
i ij j i
j
m
= +
=
( )
1
Each unit is a perceptron
70
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71. Properties of architecture
• No connections within a layer
• No direct connections between input and output layers
• Fully connected between layers
•
y f w x b
i ij j i
j
m
= +
=
( )
1
Each unit is a perceptron
71
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72. Properties of architecture
• No connections within a layer
• No direct connections between input and output layers
• Fully connected between layers
• Often more than 3 layers
• Number of output units need not equal number of input units
• Number of hidden units per layer can be more or less than
input or output units
y f w x b
i ij j i
j
m
= +
=
( )
1
Each unit is a perceptron
Often include bias as an extra weight
72
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73. What do each of the layers do?
1st layer draws
linear boundaries
2nd layer combines
the boundaries
3rd layer can generate
arbitrarily complex
boundaries
73
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74. Backward pass phase: computes ‘error signal’, propagates
the error backwards through network starting at output units
(where the error is the difference between actual and desired
output values)
Forward pass phase: computes ‘functional signal’, feed forward
propagation of input pattern signals through network
Backpropagation Learning Algorithm ‘BP’
Solution to credit assignment problem in MLP. Rumelhart, Hinton and
Williams (1986) (though actually invented earlier in a PhD thesis
relating to economics)
BP has two phases:
74
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75. Conceptually: Forward Activity -
Backward Error
75
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76. Conceptually: Forward Activity -
Backward Error
76
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77. MLP – with Single Hidden Layer
77
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https://www.cse.unsw.edu.au/~cs9417ml/MLP2/BackPropagation.html
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
78. Forward Propagation of Activity
• Step 1: Initialize weights at random, choose a
learning rate η
• Until network is trained:
• For each training example i.e. input pattern and
target output(s):
• Step 2: Do forward pass through net (with fixed
weights) to produce output(s)
– i.e., in Forward Direction, layer by layer:
• Inputs applied
• Multiplied by weights
• Summed
• Squashed by sigmoid activation function
• Output passed to each neuron in next layer
– Repeat above until network output(s) produced
78
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79. Step 3. Back-propagation of error
• Compute error (delta or local gradient) for each
output unit δ k
• Layer-by-layer, compute error (delta or local
gradient) for each hidden unit δ j by backpropagating
errors (as shown previously)
Step 4: Next, update all the weights Δwij
By gradient descent, and go back to Step 2
− The overall MLP learning algorithm, involving
forward pass and backpropagation of error
(until the network training completion), is
known as the Generalised Delta Rule (GDR),
or more commonly, the Back Propagation
(BP) algorithm
79
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80. Back Propagation Algorithm Summary
80
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81. MLP/BP: A worked example
81
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82. Worked example: Forward Pass
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83. Worked example: Forward Pass
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84. Worked example: Backward Pass
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85. Worked example: Update Weights
Using Generalized Delta Rule (BP)
85
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86. Similarly for the all weights wij:
86
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87. Verification that it works
87
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88. Training
• This was a single iteration of back-prop
• Training requires many iterations with many
training examples or epochs (one epoch is entire
presentation of complete training set)
• It can be slow !
• Note that computation in MLP is local (with
respect to each neuron)
• Parallel computation implementation is also
possible
88
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89. Training and testing data
• How many examples ?
– The more the merrier !
• Disjoint training and testing data sets
– learn from training data but evaluate
performance (generalization ability) on
unseen test data
• Aim: minimize error on test data
89
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91. August 27, 2022
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