Deep neural networks - PowerPoint PPT Presentation


Computational Physics (Lecture 18)

Neural networks explained with the example of feedforward vs. recurrent networks. Feedforward networks propagate data, while recurrent models allow loops for cascade effects. Recurrent networks are less influential but closer to the brain's function. Introduction to handwritten digit classification

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Introduction to Deep Learning: Neural Networks and Multilayer Perceptrons

Explore the fundamentals of neural networks, including artificial neurons and activation functions, in the context of deep learning. Learn about multilayer perceptrons and their role in forming decision regions for classification tasks. Understand forward propagation and backpropagation as essential

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Block-grained Scaling of Deep Neural Networks for Mobile Vision

This presentation explores the challenges of optimizing Deep Neural Networks (DNN) for mobile vision systems due to their large size and high energy consumption. The LegoDNN framework introduces a block-grained scaling approach to reduce memory access energy consumption by compressing DNNs. The agen

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Understanding Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)

Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) are powerful tools for sequential data learning, mimicking the persistent nature of human thoughts. These neural networks can be applied to various real-life applications such as time-series data prediction, text sequence processing,

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Understanding Mechanistic Interpretability in Neural Networks

Delve into the realm of mechanistic interpretability in neural networks, exploring how models can learn human-comprehensible algorithms and the importance of deciphering internal features and circuits to predict and align model behavior. Discover the goal of reverse-engineering neural networks akin

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Graph Neural Networks

Graph Neural Networks (GNNs) are a versatile form of neural networks that encompass various network architectures like NNs, CNNs, and RNNs, as well as unsupervised learning models such as RBM and DBNs. They find applications in diverse fields such as object detection, machine translation, and drug d

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Understanding Keras Functional API for Neural Networks

Explore the Keras Functional API for building complex neural network models that go beyond sequential structures. Learn how to create computational graphs, handle non-sequential models, and understand the directed graph of computations involved in deep learning. Discover the flexibility and power of

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Understanding Artificial Neural Networks From Scratch

Learn how to build artificial neural networks from scratch, focusing on multi-level feedforward networks like multi-level perceptrons. Discover how neural networks function, including training large networks in parallel and distributed systems, and grasp concepts such as learning non-linear function

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Understanding Back-Propagation Algorithm in Neural Networks

Artificial Neural Networks aim to mimic brain processing. Back-propagation is a key method to train these networks, optimizing weights to minimize loss. Multi-layer networks enable learning complex patterns by creating internal representations. Historical background traces the development from early

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A Deep Dive into Neural Network Units and Language Models

Explore the fundamentals of neural network units in language models, discussing computation, weights, biases, and activations. Understand the essence of weighted sums in neural networks and the application of non-linear activation functions like sigmoid, tanh, and ReLU. Dive into the heart of neural

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Assistive Speech System for Individuals with Speech Impediments Using Neural Networks

Individuals with speech impediments face challenges with speech-to-text software, and this paper introduces a system leveraging Artificial Neural Networks to assist. The technology showcases state-of-the-art performance in various applications, including speech recognition. The system utilizes featu

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Advancing Physics-Informed Machine Learning for PDE Solving

Explore the need for numerical methods in solving partial differential equations (PDEs), traditional techniques, neural networks' functioning, and the comparison between standard neural networks and physics-informed neural networks (PINN). Learn about the advantages, disadvantages of PINN, and ongoi

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Exploring Biological Neural Network Models

Understanding the intricacies of biological neural networks involves modeling neurons and synapses, from the passive membrane to advanced integrate-and-fire models. The quality of these models is crucial in studying the behavior of neural networks.

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Exploring Neural Quantum States and Symmetries in Quantum Mechanics

This article delves into the intricacies of anti-symmetrized neural quantum states and the application of neural networks in solving for the ground-state wave function of atomic nuclei. It discusses the setup using the Rayleigh-Ritz variational principle, neural quantum states (NQSs), variational pa

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Learning a Joint Model of Images and Captions with Neural Networks

Modeling the joint density of images and captions using neural networks involves training separate models for images and word-count vectors, then connecting them with a top layer for joint training. Deep Boltzmann Machines are utilized for further joint training to enhance each modality's layers. Th

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Understanding Spiking Neurons and Spiking Neural Networks

Spiking neural networks (SNNs) are a new approach modeled after the brain's operations, aiming for low-power neurons, billions of connections, and high accuracy training algorithms. Spiking neurons have unique features and are more energy-efficient than traditional artificial neural networks. Explor

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Role of Presynaptic Inhibition in Stabilizing Neural Networks

Presynaptic inhibition plays a crucial role in stabilizing neural networks by rapidly counteracting recurrent excitation in the face of plasticity. This mechanism prevents runaway excitation and maintains network stability, as demonstrated in computational models by Laura Bella Naumann and Henning S

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Understanding Word2Vec: Creating Dense Vectors for Neural Networks

Word2Vec is a technique used to create dense vectors to represent words in neural networks. By distinguishing target and context words, the network input and output layers are defined. Through training, the neural network predicts target words and minimizes loss. The hidden layer's neuron count dete

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Strategies for Improving Generalization in Neural Networks

Overfitting in neural networks occurs due to the model fitting both real patterns and sampling errors in the training data. The article discusses ways to prevent overfitting, such as using different models, adjusting model capacity, and controlling neural network capacity through various methods lik

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Introduction to Neural Networks in IBM SPSS Modeler 14.2

This presentation provides an introduction to neural networks in IBM SPSS Modeler 14.2. It covers the concepts of directed data mining using neural networks, the structure of neural networks, terms associated with neural networks, and the process of inputs and outputs in neural network models. The d

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Efficient Deep Neural Networks: From SqueezeNet to SqueezeBERT

Developing efficient deep neural networks has seen significant progress in the past few years, demonstrated by advancements like SqueezeNet and SqueezeBERT. This article delves into the insights gained, the intersection of Computer Vision and Natural Language Processing, key tasks in computer vision

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Detecting Image Steganography Using Neural Networks

This project focuses on utilizing neural networks to detect image steganography, specifically targeting the F5 algorithm. The team aims to develop a model that is capable of detecting and cleaning hidden messages in images without relying on hand-extracted features. They use a dataset from Kaggle co

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Distillation as a Defense Against Adversarial Perturbations in Deep Neural Networks

Deep Learning has shown great performance in various machine learning tasks, especially classification. However, adversarial samples can manipulate neural networks into misclassifying inputs, posing serious risks such as autonomous vehicle accidents. Distillation, a training technique, is proposed a

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Convolutional Neural Networks for Sentence Classification: A Deep Learning Approach

Deep learning models, originally designed for computer vision, have shown remarkable success in various Natural Language Processing (NLP) tasks. This paper presents a simple Convolutional Neural Network (CNN) architecture for sentence classification, utilizing word vectors from an unsupervised neura

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Enhancing Sea Surface Temperature Data Using Hadoop-Based Neural Networks

Large-scale sea surface temperature (SST) data are crucial for analyzing vast amounts of information, but face challenges such as data scale, system load, and noise. A Hadoop-based Backpropagation Neural Network framework processes SST data efficiently using a Backpropagation algorithm. The system p

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Understanding Advanced Classifiers and Neural Networks

This content explores the concept of advanced classifiers like Neural Networks which compose complex relationships through combining perceptrons. It delves into the workings of the classic perceptron and how modern neural networks use more complex decision functions. The visuals provided offer a cle

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Neural Network Control for Seismometer Temperature Stabilization

Utilizing neural networks, this project aims to enhance seismometer temperature stabilization by implementing nonlinear control to address system nonlinearities. The goal is to improve control performance, decrease overshoot, and allow adaptability to unpredictable parameters. The implementation of

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Understanding Batch Normalization in Neural Networks

Batch Normalization (BN) is a technique used in neural networks to improve training efficiency by reducing internal covariate shift. This process involves normalizing input data to specific ranges or mean and variance values, allowing for faster convergence in optimization algorithms. By standardizi

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Neural Networks for Learning Relational Information

Explore how neural networks can be used to learn relational information, such as family trees and connections, through examples and tasks presented by Geoffrey Hinton and the team. The content delves into predicting relationships, capturing knowledge, and representing features within neural networks

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Machine Learning and Artificial Neural Networks for Face Verification: Overview and Applications

In the realm of computer vision, the integration of machine learning and artificial neural networks has enabled significant advancements in face verification tasks. Leveraging the brain's inherent pattern recognition capabilities, AI systems can analyze vast amounts of data to enhance face detection

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Understanding Network Analysis: Whole Networks vs. Ego Networks

Explore the differences between Whole Networks and Ego Networks in social network analysis. Whole Networks provide comprehensive information about all nodes and links, enabling the computation of network-level statistics. On the other hand, Ego Networks focus on a sample of nodes, limiting the abili

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Exploring Efficient Hardware Architectures for Deep Neural Network Processing

Discover new hardware architectures designed for efficient deep neural network processing, including SCNN accelerators for compressed-sparse Convolutional Neural Networks. Learn about convolution operations, memory size versus access energy, dataflow decisions for reuse, and Planar Tiled-Input Stati

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Evolution of Neural Networks through Neuroevolution by Ken Stanley

Ken Stanley, a prominent figure in neuroevolution, has made significant contributions to the field, such as co-inventing NEAT and HyperNEAT. Through neuroevolution, complex artifacts like neural networks evolve, with the most complex known to have 100 trillion connections. The combination of evoluti

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Introduction to Neural Networks in Machine Learning

Explore the fundamentals of neural networks in machine learning, covering topics such as activation functions, architecture, training techniques, and practical applications. Discover how neural networks can approximate continuous functions with hidden units and understand the biological inspiration

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Exploring Google's Tensor Processing Unit (TPU) and Deep Neural Networks in Parallel Computing

Delve into the world of Google's TPU and deep neural networks as key solutions for speech recognition, search ranking, and more. Learn about domain-specific architectures, the structure of neural networks, and the essence of matrix multiplication in parallel computing.

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Understanding Neural Network Watermarking Technologies

Neural networks are being deployed in various domains like autonomous systems, but protecting their integrity is crucial due to the costly nature of machine learning. Watermarking provides a solution to ensure traceability, integrity, and functionality of neural networks by allowing imperceptible da

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Overview of Speech Recognition, Neural Networks, and Acoustic Models

This content delves into various topics such as speech recognition, deep learning, neural networks, and acoustic models. It covers the use of maxout networks, bootstrap aggregation, and explains why maxout works. Additionally, it explores the application of models like HMMs and discusses the differe

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Exploring Noisy Output in Neural Networks: From Escape Rate to Soft Threshold

Delve into the intricacies of noisy output in neural networks through topics such as the variation of membrane potential with white noise approximation, autocorrelation of Poisson processes, and the effects of noise on integrate-and-fire systems, both superthreshold and subthreshold. This exploratio

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Exploring Membrane Potential Variations in Neural Networks

Delve into the dynamics of membrane potential variations in neural networks through topics like white noise approximation, autocorrelation of Poisson processes, and the Noisy Integrate-and-Fire model. Investigate how these variations manifest at different thresholds, shedding light on the biological

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New Approaches in Learning Complex-Valued Neural Networks

This study explores innovative methods in training complex-valued neural networks, including a model of complex-valued neurons, network architecture, error analysis, Adam optimizer, gradient calculation, and activation function selection. Simulation results compare real-valued and complex-valued net

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