Deep Learning

2019 ◽  
pp. 130-158 ◽  
Author(s):  
Ramgopal Kashyap
Keyword(s):  
Deep Learning ◽  
Neural Systems ◽  
Review Period ◽  
Remote Part

The vast majority of the examination on profound neural systems so far has been centered on acquiring higher exactness levels by building progressively vast and profound structures. Preparing and assessing these models is just practical when a lot of assets; for example, handling power and memory are easy run of the mill applications that could profit by these models. The system starts handling the compelled gadget and depends on the remote part when the neighborhood part does not give a sufficiently precise outcome. The falling system takes into account a new ceasing component amid the review period of the system. This chapter empowers an entire assortment of independent frameworks where sensors, actuators, and registering hubs can cooperate and demonstrate that the falling design takes into account a free change in assessment speed on obliged gadgets while the misfortune in precision is kept to a base.

Implementation of Deep Learning Neural Network for Retinal Images

2020 ◽  
pp. 77-95
Author(s):  
R. Murugan
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Neural System ◽  
Neural Systems ◽  
Retinal Images ◽  
Manual Segmentation ◽  
Convolutional Network ◽  
The World ◽  
Deep Learning Neural Network ◽  
Wide Assortment

The retinal parts segmentation has been recognized as a key component in both ophthalmological and cardiovascular sickness analysis. The parts of retinal pictures, vessels, optic disc, and macula segmentations, will add to the indicative outcome. In any case, the manual segmentation of retinal parts is tedious and dreary work, and it additionally requires proficient aptitudes. This chapter proposes a supervised method to segment blood vessel utilizing deep learning methods. All the more explicitly, the proposed part has connected the completely convolutional network, which is normally used to perform semantic segmentation undertaking with exchange learning. The convolutional neural system has turned out to be an amazing asset for a few computer vision assignments. As of late, restorative picture investigation bunches over the world are rapidly entering this field and applying convolutional neural systems and other deep learning philosophies to a wide assortment of uses, and uncommon outcomes are rising constantly.

scholarly journals DNNBrain: A Unifying Toolbox for Mapping Deep Neural Networks and Brains

2020 ◽  
Vol 14 ◽  
Author(s):  
Xiayu Chen ◽  
Ming Zhou ◽  
Zhengxin Gong ◽  
Wei Xu ◽  
Xingyu Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Neuroscience ◽  
Deep Neural Networks ◽  
Learning Strategy ◽  
Neural Systems ◽  
Levels Of Abstraction ◽  
Internal Representations ◽  
Internal Operations ◽  
High Level

Deep neural networks (DNNs) have attained human-level performance on dozens of challenging tasks via an end-to-end deep learning strategy. Deep learning allows data representations that have multiple levels of abstraction; however, it does not explicitly provide any insights into the internal operations of DNNs. Deep learning's success is appealing to neuroscientists not only as a method for applying DNNs to model biological neural systems but also as a means of adopting concepts and methods from cognitive neuroscience to understand the internal representations of DNNs. Although general deep learning frameworks, such as PyTorch and TensorFlow, could be used to allow such cross-disciplinary investigations, the use of these frameworks typically requires high-level programming expertise and comprehensive mathematical knowledge. A toolbox specifically designed as a mechanism for cognitive neuroscientists to map both DNNs and brains is urgently needed. Here, we present DNNBrain, a Python-based toolbox designed for exploring the internal representations of DNNs as well as brains. Through the integration of DNN software packages and well-established brain imaging tools, DNNBrain provides application programming and command line interfaces for a variety of research scenarios. These include extracting DNN activation, probing and visualizing DNN representations, and mapping DNN representations onto the brain. We expect that our toolbox will accelerate scientific research by both applying DNNs to model biological neural systems and utilizing paradigms of cognitive neuroscience to unveil the black box of DNNs.

scholarly journals Generalizable Machine Learning in Neuroscience Using Graph Neural Networks

2021 ◽  
Vol 4 ◽  
Author(s):  
Paul Y. Wang ◽  
Sandalika Sapra ◽  
Vivek Kurien George ◽  
Gabriel A. Silva
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Neural Systems ◽  
Graph Structure ◽  
Imaging Data ◽  
C Elegans ◽  
State Classification ◽  
Graph Neural Networks ◽  
Emergent Behaviors

Although a number of studies have explored deep learning in neuroscience, the application of these algorithms to neural systems on a microscopic scale, i.e. parameters relevant to lower scales of organization, remains relatively novel. Motivated by advances in whole-brain imaging, we examined the performance of deep learning models on microscopic neural dynamics and resulting emergent behaviors using calcium imaging data from the nematode C. elegans. As one of the only species for which neuron-level dynamics can be recorded, C. elegans serves as the ideal organism for designing and testing models bridging recent advances in deep learning and established concepts in neuroscience. We show that neural networks perform remarkably well on both neuron-level dynamics prediction and behavioral state classification. In addition, we compared the performance of structure agnostic neural networks and graph neural networks to investigate if graph structure can be exploited as a favourable inductive bias. To perform this experiment, we designed a graph neural network which explicitly infers relations between neurons from neural activity and leverages the inferred graph structure during computations. In our experiments, we found that graph neural networks generally outperformed structure agnostic models and excel in generalization on unseen organisms, implying a potential path to generalizable machine learning in neuroscience.

Sentiment Mining and Analysis over Text Corpora via Complex Deep Learning Naural Architectures

2021 ◽  
Vol 2 (4) ◽  
pp. 448-461
Author(s):  
Teresa Alcamo ◽  
Alfredo Cuzzocrea ◽  
Giovanni Pilato ◽  
Daniele Schicchi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Deep Learning ◽  
Neural Systems ◽  
Italian Language ◽  
Text Corpora ◽  
Sentiment Mining ◽  
Computational Advantage ◽  
Computing Infrastructure

We analyze and compare five deep-learning neural architectures to manage the problem of irony and sarcasm detection for the Italian language. We briefly analyze the model architectures to choose the best compromise between performances and complexity. The obtained results show the effectiveness of such systems to handle the problem by achieving 93\% of F1-Score in the best case. As a case study, we also illustrate a possible embedding of the neural systems in a cloud computing infrastructure to exploit the computational advantage of using such an approach in tackling big data.

scholarly journals Signature Authentication using Deep Learning

2019 ◽  
Vol 8 (9S3) ◽  
pp. 531-535
Keyword(s):  
Deep Learning ◽  
Neural Systems ◽  
Feed Forward

This paper depicts the strategy to confirm marks utilizing profound learning. The paper covers the different modules and the design required to accomplish the reason. Convolutional neural systems are actualized to parse marks and feed forward neural systems are executed to investigate the attributes of the mark. Angle plummet is utilized to address mistakes with a technique got back to spread. This paper discusses how the mark on the check is contrasted with the mark in the database and how a last verification score is given to the client

scholarly journals Deep Learning Analysis: A Review

2018 ◽  
Vol 7 (S1) ◽  
pp. 24-28
Author(s):  
P. Rajendra Kumar ◽  
Suban Ravichandran ◽  
Narayana Satyala
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Neural Systems ◽  
Learning Methods ◽  
Learning Capacity ◽  
Abnormal State ◽  
To Come ◽  
Learning Analysis ◽  
Highlight Extraction

Deep learning is a rising territory of machine learning (ML) inquires about. It includes different shrouded layers of fake neural systems. Deep learning (DL) is a part of machine learning dependent on an arrangement of calculations that endeavor to show abnormal state reflections in information. It is utilized by Google in its voice and picture acknowledgment calculations, by Netflix and Amazon to choose what you need to watch or purchase straightaway, and by specialists at MIT to anticipate what’s to come. Profound Learning is utilized in different fields for accomplishing various levels of deliberation like sound, content; pictures highlight extraction and so forth. The Deep learning philosophy applies nonlinear changes and model reflections of abnormal state in extensive databases. With Deep learning capacity to make forecasts and groupings taking the upside of huge information, it can be a creative answer for issues and issues that have been never thought to be understood in such a simple way. Then again, it makes numerous difficulties on the researchers who are endeavoring to convey such another methodology. The accompanying audit sequentially shows how and in what real applications profound realizing calculations have been used. We have completed a broad writing audit and reviewed the utilization of profound learning methods on different fields.

scholarly journals Speech Emotion Recognition Using Deep Learning Techniques

2016 ◽  
Vol 5 (2) ◽  
pp. 113-122 ◽  
Author(s):  
Apoorva Ganapathy ◽  
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
Speech Emotion Recognition ◽  
Neural Systems ◽  
Emotion Detection ◽  
Learning Methods ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
Learning Techniques ◽  
Actual Speech

The developments in neural systems and the high demand requirement for exact and close actual Speech Emotion Recognition in human-computer interfaces mark it compulsory to liken existing methods and datasets in speech emotion detection to accomplish practicable clarifications and a securer comprehension of this unrestricted issue. The present investigation assessed deep learning methods for speech emotion detection with accessible datasets, tracked by predictable machine learning methods for SER. Finally, we present-day a multi-aspect assessment between concrete neural network methods in SER. The objective of this investigation is to deliver a review of the area of distinct SER.

Emotion Detection using Social Media Data

2021 ◽  
Vol 9 (11) ◽  
pp. 1456-1459
Author(s):  
Ansh Mehta
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Contextual Information ◽  
Latent Semantic Indexing ◽  
Neural Systems ◽  
Data Sets ◽  
Main Question ◽  
Semantic Indexing ◽  
Emotion Analysis ◽  
Twitter Users

Abstract: Previous research on emotion recognition of Twitter users centered on the use of lexicons and basic classifiers on pack of words models, despite the recent accomplishments of deep learning in many disciplines of natural language processing. The study's main question is if deep learning can help them improve their performance. Because of the scant contextual information that most posts offer, emotion analysis is still difficult. The suggested method can capture more emotion sematic than existing models by projecting emoticons and words into emoticon space, which improves the performance of emotion analysis. In a microblog setting, this aids in the detection of subjectivity, polarity, and emotion. It accomplishes this by utilizing hash tags to create three large emotion-labeled data sets that can be compared to various emotional orders. Then compare the results of a few words and character-based repetitive and convolutional neural networks to the results of a pack of words and latent semantic indexing models. Furthermore, the specifics examine the transferability of the most recent hidden state representations across distinct emotional classes and whether it is possible to construct a unified model for predicting each of them using a common representation. It's been shown that repetitive neural systems, especially character-based ones, outperform pack-of-words and latent semantic indexing models. The semantics of the token must be considered while classifying the tweet emotion. The semantics of the tokens recorded in the hash map may be simply searched. Despite these models' low exchange capacities, the recently presented training heuristic produces a unity model with execution comparable to the three solo models. Keywords: Hashtags, Sentiment Analysis, Facial Recognition, Emotions.

scholarly journals DNNBrain: a unifying toolbox for mapping deep neural networks and brains

2020 ◽  
Author(s):  
Xiayu Chen ◽  
Ming Zhou ◽  
Zhengxin Gong ◽  
Wei Xu ◽  
Xingyu Liu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Neuroscience ◽  
Deep Neural Networks ◽  
Learning Strategy ◽  
Neural Systems ◽  
Levels Of Abstraction ◽  
Internal Representations ◽  
High Level ◽  
The Brain

ABSTRACTDeep neural networks (DNNs) have attained human-level performance on dozens of challenging tasks through an end-to-end deep learning strategy. Deep learning gives rise to data representations with multiple levels of abstraction; however, it does not explicitly provide any insights into the internal operations of DNNs. Its success appeals to neuroscientists not only to apply DNNs to model biological neural systems, but also to adopt concepts and methods from cognitive neuroscience to understand the internal representations of DNNs. Although general deep learning frameworks such as PyTorch and TensorFlow could be used to allow such cross-disciplinary studies, the use of these frameworks typically requires high-level programming expertise and comprehensive mathematical knowledge. A toolbox specifically designed for cognitive neuroscientists to map DNNs and brains is urgently needed. Here, we present DNNBrain, a Python-based toolbox designed for exploring internal representations in both DNNs and the brain. By integrating DNN software packages and well-established brain imaging tools, DNNBrain provides application programming and command line interfaces for a variety of research scenarios, such as extracting DNN activation, probing DNN representations, mapping DNN representations onto the brain, and visualizing DNN representations. We expect that our toolbox will accelerate scientific research in applying DNNs to model biological neural systems and utilizing paradigms of cognitive neuroscience to unveil the black box of DNNs.

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