This ppt explains about neural networks. Applications of neural networks are discussed here.How Google uses the Artificial neural network.TensorFlow and it's description. Working of artificial neural network is discussed.
This document provides an overview of neural networks, including their history, components, connection types, learning methods, applications, and comparison to conventional computers. It discusses how biological neurons inspired the development of artificial neurons and neural networks. The key components of biological and artificial neurons are described. Connection types in neural networks include static feedforward and dynamic feedbackward connections. Learning methods include supervised, unsupervised, and reinforcement learning. Applications span mobile computing, forecasting, character recognition, and more. Neural networks learn by example rather than requiring explicitly programmed algorithms.
Neural networks are inspired by biological neural systems. An artificial neural network (ANN) is an information processing paradigm that is modeled after the human brain. ANNs learn by example, through a learning process, like the way synapses strengthen in the human brain. An ANN is composed of interconnected processing nodes that work together to solve problems. It can be trained to perform tasks by considering examples without being explicitly programmed.
The document provides an overview of Ray Kurzweil's book 'How to Create a Mind'. The summary discusses the following key points:
1. The book covers the structure of the neocortex and pattern recognition theory of mind, solving speech recognition problems using vector quantization, and simulating brains.
2. It also discusses Watson, an AI system that can answer questions in natural language and won Jeopardy! in 2011, and the Blue Brain project which aims to synthesize a human brain through high-precision scanning of neural tissue.
3. Vector quantization, a technique that models probability densities by distributing prototype vectors, is discussed as a method for solving speech recognition problems.
From Sensor Data to Triples: Information Flow in Semantic Sensor NetworksNikolaos Konstantinou
Sensor networks constitute a technological approach of increasing popularity when it comes to monitoring an area, offering context-aware solutions. This shift from Desktop computing to Ubiquitous computing entails numerous options and challenges in designing, implementing and shaping the behavior of systems that consume, integrate, fuse and exploit sensor data. Things tend to be more complicated when, in order to extract meaning from the collected information, the Semantic Web paradigm is adopted. In this talk, we discuss the information flow in systems that collect sensor data into semantically annotated repositories. Specifically, we analyze the journey that information makes, from its capture as electromagnetic pulses by the sensors, to its storage as a semantic web triples, along with its semantics in the system’s knowledge base. We introduce the main related concepts, we analyze the main components that such systems comprise, the choices that can be made, and the respective benefits, drawbacks, and effect to the overall system properties.
This document provides an overview of artificial neural networks. It discusses how ANNs are inspired by biological neural systems and composed of interconnected processing elements called neurons. ANNs are configured through a learning process to perform tasks like pattern recognition or data classification. The document outlines the basic components of ANNs, including different types of network architectures like feedforward and feedback networks. It provides examples of applications for ANNs, such as speech and image recognition. In conclusion, it discusses using ANNs for applications in fields like medicine and business.
- Artificial neural networks are computational models inspired by the human brain that are composed of interconnected nodes (neurons) that can learn from examples.
- Knowledge is acquired by adjusting the synaptic connections between neurons based on a learning process. These connections store the knowledge gained during learning.
- There are different network architectures including single-layer feedforward networks, multi-layer feedforward networks, and recurrent networks. Activation functions determine whether a neuron is active.
- Applications of ANNs include pattern recognition, function approximation, and associative memory. Common current models are deep learning architectures and multilayer perceptrons.
This document provides an overview of neural networks, including their history, components, connection types, learning methods, applications, and comparison to conventional computers. It discusses how biological neurons inspired the development of artificial neurons and neural networks. The key components of biological and artificial neurons are described. Connection types in neural networks include static feedforward and dynamic feedbackward connections. Learning methods include supervised, unsupervised, and reinforcement learning. Applications span mobile computing, forecasting, character recognition, and more. Neural networks learn by example rather than requiring explicitly programmed algorithms.
Neural networks are inspired by biological neural systems. An artificial neural network (ANN) is an information processing paradigm that is modeled after the human brain. ANNs learn by example, through a learning process, like the way synapses strengthen in the human brain. An ANN is composed of interconnected processing nodes that work together to solve problems. It can be trained to perform tasks by considering examples without being explicitly programmed.
The document provides an overview of Ray Kurzweil's book 'How to Create a Mind'. The summary discusses the following key points:
1. The book covers the structure of the neocortex and pattern recognition theory of mind, solving speech recognition problems using vector quantization, and simulating brains.
2. It also discusses Watson, an AI system that can answer questions in natural language and won Jeopardy! in 2011, and the Blue Brain project which aims to synthesize a human brain through high-precision scanning of neural tissue.
3. Vector quantization, a technique that models probability densities by distributing prototype vectors, is discussed as a method for solving speech recognition problems.
From Sensor Data to Triples: Information Flow in Semantic Sensor NetworksNikolaos Konstantinou
Sensor networks constitute a technological approach of increasing popularity when it comes to monitoring an area, offering context-aware solutions. This shift from Desktop computing to Ubiquitous computing entails numerous options and challenges in designing, implementing and shaping the behavior of systems that consume, integrate, fuse and exploit sensor data. Things tend to be more complicated when, in order to extract meaning from the collected information, the Semantic Web paradigm is adopted. In this talk, we discuss the information flow in systems that collect sensor data into semantically annotated repositories. Specifically, we analyze the journey that information makes, from its capture as electromagnetic pulses by the sensors, to its storage as a semantic web triples, along with its semantics in the system’s knowledge base. We introduce the main related concepts, we analyze the main components that such systems comprise, the choices that can be made, and the respective benefits, drawbacks, and effect to the overall system properties.
This document provides an overview of artificial neural networks. It discusses how ANNs are inspired by biological neural systems and composed of interconnected processing elements called neurons. ANNs are configured through a learning process to perform tasks like pattern recognition or data classification. The document outlines the basic components of ANNs, including different types of network architectures like feedforward and feedback networks. It provides examples of applications for ANNs, such as speech and image recognition. In conclusion, it discusses using ANNs for applications in fields like medicine and business.
- Artificial neural networks are computational models inspired by the human brain that are composed of interconnected nodes (neurons) that can learn from examples.
- Knowledge is acquired by adjusting the synaptic connections between neurons based on a learning process. These connections store the knowledge gained during learning.
- There are different network architectures including single-layer feedforward networks, multi-layer feedforward networks, and recurrent networks. Activation functions determine whether a neuron is active.
- Applications of ANNs include pattern recognition, function approximation, and associative memory. Common current models are deep learning architectures and multilayer perceptrons.
Privacy preserving back propagation neural network learning over arbitrarily ...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Neural networks are computational models inspired by the human brain. They consist of interconnected nodes that process information using a principle called neural learning. The document discusses the history and evolution of neural networks. It also provides examples of applications like image recognition, medical diagnosis, and predictive analytics. Neural networks are well-suited for problems that are difficult to solve with traditional algorithms like pattern recognition and classification.
1. The document describes an introductory course on neural networks. It includes information on topics covered, textbooks, assignments, and report topics.
2. The main topics covered are comprehensive introduction, learning algorithms, and types of neural networks. Report topics include the McCulloch-Pitts model, applications of neural networks, and various learning algorithms.
3. The document also provides background information on biological neural networks and the basic components and functioning of artificial neural networks at a high level.
In this Chapter, we focus on dealing with data originating from sensor data streams, in order to materialize an intelligent, semantically-enabled data layer. First, we introduce the concepts that are covered in this Section: real-time, context-awareness, windowing, information fusion. Next, we mention the difficulties associated with the attempt of creating a semantic sensor network, we note our architectural concerns by presenting a number of issues that have to be dealt with when designing a system for the real-time information integration from distributed data sources and sensors. Finally, the anatomy of a system for the end-to-end data multi-sensor data fusion and semantic enrichment is illustrated, while the end-to-end information flow and respective steps are analyzed.
This document discusses artificial neural networks. It begins with an introduction and overview of the history and biological neuron model. It then explains the artificial neuron model and different learning methods like backpropagation. Applications of neural networks in areas like character recognition and stock market prediction are provided. The document concludes by discussing the advantages of neural networks like their ability to handle large amounts of data and learn continuously.
Learning scientific scholar representations using a combination of collaborat...Ankush Khandelwal
Aim of the project is to learn vector representations for authors who publish scientific research papers . The representations should be such that authors who work in same domain ( i.e. same research area ) must be closer in vector space. These representations helps to categorize or cluster authors into various categories and further predict future collaboration based on past data.
Artificial neural networks (ANNs) are a machine learning approach modeled after the human brain. ANNs consist of artificial neurons that are connected in a network similar to biological neurons. Each neuron receives inputs, applies an activation function, and outputs a value. ANNs are specified by their neuron model, architecture including connections between neurons with weights, and a learning algorithm to train the network by modifying weights. ANNs have advantages like storing information on the entire network, working with incomplete knowledge, fault tolerance, and parallel processing. However, they also have disadvantages such as hardware dependence, unexplained behavior, difficulty determining network structure, and unknown optimal training duration.
Neural networks are computational models inspired by the human brain that are used for machine learning. They consist of interconnected nodes that process information using a learning algorithm. Neural networks are used for applications like pattern recognition and classification. The first neural networks were developed in the 1940s-1950s, but modern networks use many layers of nodes, called deep learning, which has led to state-of-the-art performance in computer vision, natural language processing, and other domains. Deep learning requires large amounts of data and computational power but can automatically discover relevant features from data.
This document discusses artificial neural networks. It defines neural networks as computational models inspired by the human brain that are used for tasks like classification, clustering, and pattern recognition. The key points are:
- Neural networks contain interconnected artificial neurons that can perform complex computations. They are inspired by biological neurons in the brain.
- Common neural network types are feedforward networks, where data flows from input to output, and recurrent networks, which contain feedback loops.
- Neural networks are trained using algorithms like backpropagation that minimize error by adjusting synaptic weights between neurons.
- Neural networks have many applications including voice recognition, image recognition, robotics and more due to their ability to learn from large amounts of data.
This document discusses artificial neural networks. It defines neural networks as computational models inspired by the human brain that are used for tasks like classification, clustering, and pattern recognition. The key points are:
- Neural networks contain interconnected artificial neurons that can perform complex computations. They are inspired by biological neurons in the brain.
- Common neural network types are feedforward networks, where data flows from input to output, and recurrent networks, which contain feedback loops.
- Neural networks are trained using algorithms like backpropagation that minimize error by adjusting synaptic weights between neurons.
- Neural networks have various applications including voice recognition, image recognition, and robotics due to their ability to learn from large amounts of data.
Neural networks are programs that mimic the human brain by learning from large amounts of data. They use simulated neurons that are connected together to form networks, similar to the human nervous system. Neural networks learn by adjusting the strengths of connections between neurons, and can be used to perform tasks like pattern recognition or prediction. Common neural network training algorithms include gradient descent and backpropagation, which help minimize errors by adjusting connection weights.
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Build a simple image recognition system with tensor flowDebasisMohanty37
A perfect working model to detect mnist dataset using TensorFlow.
Dataset:
http://yann.lecun.com/exdb/mnist/
For code check the below GitHub links:
https://github.com/Jitudebz/psychic-pancake
A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.
This document provides an overview of neural networks and fuzzy systems. It outlines a course on the topic, which is divided into two parts: neural networks and fuzzy systems. For neural networks, it covers fundamental concepts of artificial neural networks including single and multi-layer feedforward networks, feedback networks, and unsupervised learning. It also discusses the biological neuron, typical neural network architectures, learning techniques such as backpropagation, and applications of neural networks. Popular activation functions like sigmoid, tanh, and ReLU are also explained.
This document provides an introduction to deep learning. It discusses the history of machine learning and how neural networks work. Specifically, it describes different types of neural networks like deep belief networks, convolutional neural networks, and recurrent neural networks. It also covers applications of deep learning, as well as popular platforms, frameworks and libraries used for deep learning development. Finally, it demonstrates an example of using the Nvidia DIGITS tool to train a convolutional neural network for image classification of car park images.
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
Abhey Sharma's presentation discusses neural networks. It defines biological neural networks as networks of real neurons in the brain, and artificial neural networks (ANNs) as artificial systems composed of interconnected nodes modeled after biological neurons. ANNs are configured through learning to perform tasks like pattern recognition. The history of neural networks is reviewed, from early enthusiasm to a period of frustration before recent resurgence. Neural networks offer advantages like adaptive learning, self-organization, fault tolerance, and fast real-time operation, but disadvantages include their "black box" nature, high computational requirements, difficulty incorporating time, and inevitable errors of approximation.
This document provides an introduction to deep learning. It begins with an overview of artificial intelligence techniques like computer vision, speech processing, and natural language processing that benefit from deep learning. It then reviews the history of deep learning algorithms from perceptrons to modern deep neural networks. The core concepts of deep learning processes, neural network architectures, and training techniques like backpropagation are explained. Popular deep learning frameworks like TensorFlow, Keras, and PyTorch are also introduced. Finally, examples of convolutional neural networks, recurrent neural networks, and generative adversarial networks are briefly described along with tips for training deep neural networks and resources for further learning.
The document discusses neural networks, including human neural networks and artificial neural networks (ANNs). It provides details on the key components of ANNs, such as the perceptron and backpropagation algorithm. ANNs are inspired by biological neural systems and are used for applications like pattern recognition, time series prediction, and control systems. The document also outlines some current uses of neural networks in areas like signal processing, anomaly detection, and soft sensors.
Privacy preserving back propagation neural network learning over arbitrarily ...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Neural networks are computational models inspired by the human brain. They consist of interconnected nodes that process information using a principle called neural learning. The document discusses the history and evolution of neural networks. It also provides examples of applications like image recognition, medical diagnosis, and predictive analytics. Neural networks are well-suited for problems that are difficult to solve with traditional algorithms like pattern recognition and classification.
1. The document describes an introductory course on neural networks. It includes information on topics covered, textbooks, assignments, and report topics.
2. The main topics covered are comprehensive introduction, learning algorithms, and types of neural networks. Report topics include the McCulloch-Pitts model, applications of neural networks, and various learning algorithms.
3. The document also provides background information on biological neural networks and the basic components and functioning of artificial neural networks at a high level.
In this Chapter, we focus on dealing with data originating from sensor data streams, in order to materialize an intelligent, semantically-enabled data layer. First, we introduce the concepts that are covered in this Section: real-time, context-awareness, windowing, information fusion. Next, we mention the difficulties associated with the attempt of creating a semantic sensor network, we note our architectural concerns by presenting a number of issues that have to be dealt with when designing a system for the real-time information integration from distributed data sources and sensors. Finally, the anatomy of a system for the end-to-end data multi-sensor data fusion and semantic enrichment is illustrated, while the end-to-end information flow and respective steps are analyzed.
This document discusses artificial neural networks. It begins with an introduction and overview of the history and biological neuron model. It then explains the artificial neuron model and different learning methods like backpropagation. Applications of neural networks in areas like character recognition and stock market prediction are provided. The document concludes by discussing the advantages of neural networks like their ability to handle large amounts of data and learn continuously.
Learning scientific scholar representations using a combination of collaborat...Ankush Khandelwal
Aim of the project is to learn vector representations for authors who publish scientific research papers . The representations should be such that authors who work in same domain ( i.e. same research area ) must be closer in vector space. These representations helps to categorize or cluster authors into various categories and further predict future collaboration based on past data.
Artificial neural networks (ANNs) are a machine learning approach modeled after the human brain. ANNs consist of artificial neurons that are connected in a network similar to biological neurons. Each neuron receives inputs, applies an activation function, and outputs a value. ANNs are specified by their neuron model, architecture including connections between neurons with weights, and a learning algorithm to train the network by modifying weights. ANNs have advantages like storing information on the entire network, working with incomplete knowledge, fault tolerance, and parallel processing. However, they also have disadvantages such as hardware dependence, unexplained behavior, difficulty determining network structure, and unknown optimal training duration.
Neural networks are computational models inspired by the human brain that are used for machine learning. They consist of interconnected nodes that process information using a learning algorithm. Neural networks are used for applications like pattern recognition and classification. The first neural networks were developed in the 1940s-1950s, but modern networks use many layers of nodes, called deep learning, which has led to state-of-the-art performance in computer vision, natural language processing, and other domains. Deep learning requires large amounts of data and computational power but can automatically discover relevant features from data.
This document discusses artificial neural networks. It defines neural networks as computational models inspired by the human brain that are used for tasks like classification, clustering, and pattern recognition. The key points are:
- Neural networks contain interconnected artificial neurons that can perform complex computations. They are inspired by biological neurons in the brain.
- Common neural network types are feedforward networks, where data flows from input to output, and recurrent networks, which contain feedback loops.
- Neural networks are trained using algorithms like backpropagation that minimize error by adjusting synaptic weights between neurons.
- Neural networks have many applications including voice recognition, image recognition, robotics and more due to their ability to learn from large amounts of data.
This document discusses artificial neural networks. It defines neural networks as computational models inspired by the human brain that are used for tasks like classification, clustering, and pattern recognition. The key points are:
- Neural networks contain interconnected artificial neurons that can perform complex computations. They are inspired by biological neurons in the brain.
- Common neural network types are feedforward networks, where data flows from input to output, and recurrent networks, which contain feedback loops.
- Neural networks are trained using algorithms like backpropagation that minimize error by adjusting synaptic weights between neurons.
- Neural networks have various applications including voice recognition, image recognition, and robotics due to their ability to learn from large amounts of data.
Neural networks are programs that mimic the human brain by learning from large amounts of data. They use simulated neurons that are connected together to form networks, similar to the human nervous system. Neural networks learn by adjusting the strengths of connections between neurons, and can be used to perform tasks like pattern recognition or prediction. Common neural network training algorithms include gradient descent and backpropagation, which help minimize errors by adjusting connection weights.
Neural Network and Artificial Intelligence.
Neural Network and Artificial Intelligence.
WHAT IS NEURAL NETWORK?
The method calculation is based on the interaction of plurality of processing elements inspired by biological nervous system called neurons.
It is a powerful technique to solve real world problem.
A neural network is composed of a number of nodes, or units[1], connected by links. Each linkhas a numeric weight[2]associated with it. .
Weights are the primary means of long-term storage in neural networks, and learning usually takes place by updating the weights.
Artificial neurons are the constitutive units in an artificial neural network.
WHY USE NEURAL NETWORKS?
It has ability to Learn from experience.
It can deal with incomplete information.
It can produce result on the basis of input, has not been taught to deal with.
It is used to extract useful pattern from given data i.e. pattern Recognition etc.
Biological Neurons
Four parts of a typical nerve cell :• DENDRITES: Accepts the inputs• SOMA : Process the inputs• AXON : Turns the processed inputs into outputs.• SYNAPSES : The electrochemical contactbetween the neurons.
ARTIFICIAL NEURONS MODEL
Inputs to the network arerepresented by the x1mathematical symbol, xn
Each of these inputs are multiplied by a connection weight , wn
sum = w1 x1 + ……+ wnxn
These products are simplysummed, fed through the transfer function, f( ) to generate a result and then output.
NEURON MODEL
Neuron Consist of:
Inputs (Synapses): inputsignal.Weights (Dendrites):determines the importance ofincoming value.Output (Axon): output toother neuron or of NN .
Build a simple image recognition system with tensor flowDebasisMohanty37
A perfect working model to detect mnist dataset using TensorFlow.
Dataset:
http://yann.lecun.com/exdb/mnist/
For code check the below GitHub links:
https://github.com/Jitudebz/psychic-pancake
A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.
This document provides an overview of neural networks and fuzzy systems. It outlines a course on the topic, which is divided into two parts: neural networks and fuzzy systems. For neural networks, it covers fundamental concepts of artificial neural networks including single and multi-layer feedforward networks, feedback networks, and unsupervised learning. It also discusses the biological neuron, typical neural network architectures, learning techniques such as backpropagation, and applications of neural networks. Popular activation functions like sigmoid, tanh, and ReLU are also explained.
This document provides an introduction to deep learning. It discusses the history of machine learning and how neural networks work. Specifically, it describes different types of neural networks like deep belief networks, convolutional neural networks, and recurrent neural networks. It also covers applications of deep learning, as well as popular platforms, frameworks and libraries used for deep learning development. Finally, it demonstrates an example of using the Nvidia DIGITS tool to train a convolutional neural network for image classification of car park images.
Automatic Attendace using convolutional neural network Face Recognitionvatsal199567
Automatic Attendance System will recognize the face of the student through the camera in the class and mark the attendance. It was built in Python with Machine Learning.
Abhey Sharma's presentation discusses neural networks. It defines biological neural networks as networks of real neurons in the brain, and artificial neural networks (ANNs) as artificial systems composed of interconnected nodes modeled after biological neurons. ANNs are configured through learning to perform tasks like pattern recognition. The history of neural networks is reviewed, from early enthusiasm to a period of frustration before recent resurgence. Neural networks offer advantages like adaptive learning, self-organization, fault tolerance, and fast real-time operation, but disadvantages include their "black box" nature, high computational requirements, difficulty incorporating time, and inevitable errors of approximation.
This document provides an introduction to deep learning. It begins with an overview of artificial intelligence techniques like computer vision, speech processing, and natural language processing that benefit from deep learning. It then reviews the history of deep learning algorithms from perceptrons to modern deep neural networks. The core concepts of deep learning processes, neural network architectures, and training techniques like backpropagation are explained. Popular deep learning frameworks like TensorFlow, Keras, and PyTorch are also introduced. Finally, examples of convolutional neural networks, recurrent neural networks, and generative adversarial networks are briefly described along with tips for training deep neural networks and resources for further learning.
The document discusses neural networks, including human neural networks and artificial neural networks (ANNs). It provides details on the key components of ANNs, such as the perceptron and backpropagation algorithm. ANNs are inspired by biological neural systems and are used for applications like pattern recognition, time series prediction, and control systems. The document also outlines some current uses of neural networks in areas like signal processing, anomaly detection, and soft sensors.
This PPT contains entire content in short. My book on ANN under the title "SOFT COMPUTING" with Watson Publication and my classmates can be referred together.
This document provides an overview of multi-layer perceptrons (MLPs), also known as neural networks. It begins by discussing how perceptrons work, including taking inputs, multiplying them by weights, passing them through an activation function, and producing an output. MLPs consist of multiple stacked perceptron layers that allow them to solve more complex problems. Key aspects that enable deep learning with MLPs include backpropagation to optimize weights, tuning hyperparameters like the number of layers and activation functions, and using advanced training techniques involving learning rates, epochs, batches and optimizer algorithms.
Big Data Malaysia - A Primer on Deep LearningPoo Kuan Hoong
This document provides an overview of deep learning, including a brief history of machine learning and neural networks. It discusses various deep learning models such as deep belief networks, convolutional neural networks, and recurrent neural networks. Applications of deep learning in areas like computer vision, natural language processing, and robotics are also covered. Finally, popular platforms, frameworks and libraries for developing deep learning systems are mentioned.
Deep learning is introduced along with its applications and key players in the field. The document discusses the problem space of inputs and outputs for deep learning systems. It describes what deep learning is, providing definitions and explaining the rise of neural networks. Key deep learning architectures like convolutional neural networks are overviewed along with a brief history and motivations for deep learning.
Much of data is sequential – think speech, text, DNA, stock prices, financial transactions and customer action histories. Modern methods for modelling sequence data are often deep learning-based, composed of either recurrent neural networks (RNNs) or attention-based Transformers. A tremendous amount of research progress has recently been made in sequence modelling, particularly in the application to NLP problems. However, the inner workings of these sequence models can be difficult to dissect and intuitively understand.
This presentation/tutorial will start from the basics and gradually build upon concepts in order to impart an understanding of the inner mechanics of sequence models – why do we need specific architectures for sequences at all, when you could use standard feed-forward networks? How do RNNs actually handle sequential information, and why do LSTM units help longer-term remembering of information? How can Transformers do such a good job at modelling sequences without any recurrence or convolutions?
In the practical portion of this tutorial, attendees will learn how to build their own LSTM-based language model in Keras. A few other use cases of deep learning-based sequence modelling will be discussed – including sentiment analysis (prediction of the emotional valence of a piece of text) and machine translation (automatic translation between different languages).
The goals of this presentation are to provide an overview of popular sequence-based problems, impart an intuition for how the most commonly-used sequence models work under the hood, and show that quite similar architectures are used to solve sequence-based problems across many domains.
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Describing and Interpreting an Immersive Learning Case with the Immersion Cub...Leonel Morgado
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
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We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Unlocking the mysteries of reproduction: Exploring fecundity and gonadosomati...AbdullaAlAsif1
The pygmy halfbeak Dermogenys colletei, is known for its viviparous nature, this presents an intriguing case of relatively low fecundity, raising questions about potential compensatory reproductive strategies employed by this species. Our study delves into the examination of fecundity and the Gonadosomatic Index (GSI) in the Pygmy Halfbeak, D. colletei (Meisner, 2001), an intriguing viviparous fish indigenous to Sarawak, Borneo. We hypothesize that the Pygmy halfbeak, D. colletei, may exhibit unique reproductive adaptations to offset its low fecundity, thus enhancing its survival and fitness. To address this, we conducted a comprehensive study utilizing 28 mature female specimens of D. colletei, carefully measuring fecundity and GSI to shed light on the reproductive adaptations of this species. Our findings reveal that D. colletei indeed exhibits low fecundity, with a mean of 16.76 ± 2.01, and a mean GSI of 12.83 ± 1.27, providing crucial insights into the reproductive mechanisms at play in this species. These results underscore the existence of unique reproductive strategies in D. colletei, enabling its adaptation and persistence in Borneo's diverse aquatic ecosystems, and call for further ecological research to elucidate these mechanisms. This study lends to a better understanding of viviparous fish in Borneo and contributes to the broader field of aquatic ecology, enhancing our knowledge of species adaptations to unique ecological challenges.
Or: Beyond linear.
Abstract: Equivariant neural networks are neural networks that incorporate symmetries. The nonlinear activation functions in these networks result in interesting nonlinear equivariant maps between simple representations, and motivate the key player of this talk: piecewise linear representation theory.
Disclaimer: No one is perfect, so please mind that there might be mistakes and typos.
dtubbenhauer@gmail.com
Corrected slides: dtubbenhauer.com/talks.html
ESPP presentation to EU Waste Water Network, 4th June 2024 “EU policies driving nutrient removal and recycling
and the revised UWWTD (Urban Waste Water Treatment Directive)”
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
Remote Sensing and Computational, Evolutionary, Supercomputing, and Intellige...University of Maribor
Slides from talk:
Aleš Zamuda: Remote Sensing and Computational, Evolutionary, Supercomputing, and Intelligent Systems.
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Inter-Society Networking Panel GRSS/MTT-S/CIS Panel Session: Promoting Connection and Cooperation
https://www.etran.rs/2024/en/home-english/
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
2. Artificial neural networks
• During recent times deep learning and neural
have become very common in solving many
machine learning problems
• Recently it has become highly popular because of
recent increase in high computation CPU power
• It’s application is in storing and recalling data or
patterns, classifying patterns, performing the
mapping from input patterns to output patterns,
identifying and grouping similar patterns.
3. Neural Network and TensorFlow
• Google uses neural networks in almost all of
it’s technological products like Google Maps,
Google photos, Google lens, etc
• Google released it’s library for neural
networks called TensorFlow .With TensorFlow
we can easily build neural networks and easily
perform computation on convolutional neural
network
4. What is Neural Network?
• Artificial neural network is an information
processing system which is inspired by
biological neural network.
• A neural net has a large number of simple
processing units called neurons or nodes.
• Each neuron is connected to various neurons
by directed links which have an associated
weight to it.
5. Working of Neural Networks
• When a signal passes through links from one
node to another, it gets multiplied by their
respective weights.
• A neuron is connected with multiple neurons,
all the signals are added up in the node
• Then there is an activation function which
checks if the added signal value is greater than
some threshold value, if it is greater then the
signal is fired otherwise it is not.