Business Applications of Deep Learning

2020 ◽  
pp. 942-964
Author(s):  
Armando Vieira
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Video Processing ◽  
Autonomous Vehicles ◽  
Video Annotation ◽  
Business Applications ◽  
Personal Assistants ◽  
Efficient Learning ◽  
High Level ◽  
Almost All

Deep Learning (DL) took Artificial Intelligence (AI) by storm and has infiltrated into business at an unprecedented rate. Access to vast amounts of data extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges. DL is the cornerstone technology behind products for image recognition and video annotation, voice recognition, personal assistants, automated translation and autonomous vehicles. DL works similarly to the brain by extracting high-level, complex abstractions from data in a hierarchical and discriminative or generative way. The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. In this chapter, I present the most significant algorithms and applications, including Natural Language Processing (NLP), image and video processing and finance.

Business Applications of Deep Learning

2020 ◽  
pp. 440-462
Author(s):  
Armando Vieira
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Video Processing ◽  
Autonomous Vehicles ◽  
Video Annotation ◽  
Business Applications ◽  
Personal Assistants ◽  
Efficient Learning ◽  
High Level ◽  
Almost All

Deep Learning (DL) took Artificial Intelligence (AI) by storm and has infiltrated into business at an unprecedented rate. Access to vast amounts of data extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges. DL is the cornerstone technology behind products for image recognition and video annotation, voice recognition, personal assistants, automated translation and autonomous vehicles. DL works similarly to the brain by extracting high-level, complex abstractions from data in a hierarchical and discriminative or generative way. The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. In this chapter, I present the most significant algorithms and applications, including Natural Language Processing (NLP), image and video processing and finance.

Business Applications of Deep Learning

2017 ◽  
pp. 39-67 ◽  
Author(s):  
Armando Vieira
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Video Processing ◽  
Autonomous Vehicles ◽  
Video Annotation ◽  
Business Applications ◽  
Personal Assistants ◽  
Efficient Learning ◽  
High Level ◽  
Almost All

Deep Learning (DL) took Artificial Intelligence (AI) by storm and has infiltrated into business at an unprecedented rate. Access to vast amounts of data extensive computational power and a new wave of efficient learning algorithms, helped Artificial Neural Networks to achieve state-of-the-art results in almost all AI challenges. DL is the cornerstone technology behind products for image recognition and video annotation, voice recognition, personal assistants, automated translation and autonomous vehicles. DL works similarly to the brain by extracting high-level, complex abstractions from data in a hierarchical and discriminative or generative way. The implications of DL supported AI in business is tremendous, shaking to the foundations many industries. In this chapter, I present the most significant algorithms and applications, including Natural Language Processing (NLP), image and video processing and finance.

scholarly journals Road-Aware Trajectory Prediction for Autonomous Driving on Highways

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4703
Author(s):  
Yookhyun Yoon ◽  
Taeyeon Kim ◽  
Ho Lee ◽  
Jahnghyon Park
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Prediction Method ◽  
Autonomous Driving ◽  
Structural Constraints ◽  
High Definition ◽  
Trajectory Prediction ◽  
The Road ◽  
Road Geometry ◽  
Efficient Learning

For driving safely and comfortably, the long-term trajectory prediction of surrounding vehicles is essential for autonomous vehicles. For handling the uncertain nature of trajectory prediction, deep-learning-based approaches have been proposed previously. An on-road vehicle must obey road geometry, i.e., it should run within the constraint of the road shape. Herein, we present a novel road-aware trajectory prediction method which leverages the use of high-definition maps with a deep learning network. We developed a data-efficient learning framework for the trajectory prediction network in the curvilinear coordinate system of the road and a lane assignment for the surrounding vehicles. Then, we proposed a novel output-constrained sequence-to-sequence trajectory prediction network to incorporate the structural constraints of the road. Our method uses these structural constraints as prior knowledge for the prediction network. It is not only used as an input to the trajectory prediction network, but is also included in the constrained loss function of the maneuver recognition network. Accordingly, the proposed method can predict a feasible and realistic intention of the driver and trajectory. Our method has been evaluated using a real traffic dataset, and the results thus obtained show that it is data-efficient and can predict reasonable trajectories at merging sections.

Improved YOLOv3 with duplex FPN for object detection based on deep learning

2021 ◽  
pp. 002072092098352
Author(s):  
Seokyong Shin ◽  
Hyunho Han ◽  
Sang Hun Lee
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Autonomous Vehicles ◽  
Detection Accuracy ◽  
Small Object ◽  
Feature Maps ◽  
Low Level ◽  
Small Object Detection ◽  
High Level ◽  
Networks Structure

YOLOv3 is a deep learning-based real-time object detector and is mainly used in applications such as video surveillance and autonomous vehicles. In this paper, we proposed an improved YOLOv3 (You Only Look Once version 3) applied Duplex FPN, which enhanced large object detection by utilizing low-level feature information. The conventional YOLOv3 improved the small object detection performance by applying FPN (Feature Pyramid Networks) structure to YOLOv2. However, YOLOv3 with an FPN structure specialized in detecting small objects, so it is difficult to detect large objects. Therefore, this paper proposed an improved YOLOv3 applied Duplex FPN, which can utilize low-level location information in high-level feature maps instead of the existing FPN structure of YOLOv3. This improved the detection accuracy of large objects. Also, an extra detection layer was added to the top-level feature map to prevent failure of detection of parts of large objects. Further, dimension clusters of each detection layer were reassigned to learn quickly how to accurately detect objects. The proposed method was compared and analyzed in the PASCAL VOC dataset. The experimental results showed that the bounding box accuracy of large objects improved owing to the Duplex FPN and extra detection layer, and the proposed method succeeded in detecting large objects that the existing YOLOv3 did not.

scholarly journals Skin Diseases Prediction using Deep Learning Framework

2020 ◽  
Vol 8 (6) ◽  
pp. 4781-4784
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Skin Diseases ◽  
Dermatological Diseases ◽  
Learning Framework ◽  
Computer Aided ◽  
High Level ◽  
Almost All

Dermatological diseases are found to induce a serious impact on the health of millions of people as everyone is affected by almost all types of skin disorders every year. Since the human analysis of such diseases takes some time and effort, and current methods are only used to analyse singular types of skin diseases, there is a need for a more high-level computer-aided expertise in the analysis and diagnosis of multi-type skin diseases. This paper proposes an approach to use computer-aided techniques in deep learning neural networks such as Convolutional neural networks (CNN) and Residual Neural Networks (ResNet) to predict skin diseases real-time and thus provides more accuracy than other neural networks.

scholarly journals Deep Learning Accelerators: A Case Study

2020 ◽  
Author(s):  
Hamidreza Bolhasani ◽  
Somayyeh Jafarali Jassbi
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Performance Indicators ◽  
Learning Task ◽  
High Accuracy ◽  
The Other ◽  
Almost All ◽  
L1 And L2 ◽  
Accuracy Speed

Abstract In the recent years, deep learning has become one of the most important topics in computer science. Deep learning is a growing trend in the edge of technology and its applications are now seen in many aspects of our life such as object detection, speech recognition, natural language processing, etc. Currently, almost all major sciences and technologies are benefiting from the advantages of deep learning such as high accuracy, speed and flexibility. Therefore, any efforts for improving performance of related techniques is valuable. Deep learning accelerators are considered as hardware architecture, which are designed and optimized for increasing the speed, efficiency and accuracy of computers that are running deep learning algorithms. In this paper, after reviewing some backgrounds about deep learning, a well-known accelerator architecture named MAERI is investigated. By using an open source tool called MAESTRO, the performance of a deep learning task is measured and compared on two different data flow strategies: NLR and NVDLA. Measured performance indicators of novel optimized architecture, NVDLA shows higher L1 and L2 computation reuse and lower total runtime (cycles) in comparison to the other one.

scholarly journals Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently

2020 ◽  
Vol 477 (23) ◽  
pp. 4559-4580
Author(s):  
Douglas B. Kell ◽  
Soumitra Samanta ◽  
Neil Swainston
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Chemical Biology ◽  
Large Scale ◽  
Chemical Space ◽  
Molecular Structures ◽  
Crucial Issue ◽  
Recent Developments ◽  
Discrete Nature ◽  
High Level

The number of ‘small’ molecules that may be of interest to chemical biologists — chemical space — is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved ‘forward’ problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). ‘Deep’ (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.

scholarly journals Deep Learning Accelerators: A Case Study with MAESTRO

2020 ◽  
Author(s):  
Hamidreza Bolhasani ◽  
Somayyeh Jafarali Jassbi
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Performance Indicators ◽  
Learning Task ◽  
High Accuracy ◽  
Spatio Temporal ◽  
Almost All ◽  
L1 And L2 ◽  
Accuracy Speed

Abstract In recent years, deep learning has become one of the most important topics in computer sciences. Deep learning is a growing trend in the edge of technology and its applications are now seen in many aspects of our life such as object detection, speech recognition, natural language processing, etc. Currently, almost all major sciences and technologies are benefiting from the advantages of deep learning such as high accuracy, speed and flexibility. Therefore, any efforts in improving performance of related techniques is valuable. Deep learning accelerators are considered as hardware architecture, which are designed and optimized for increasing speed, efficiency and accuracy of computers that are running deep learning algorithms. In this paper, after reviewing some backgrounds on deep learning, a well-known accelerator architecture named MAERI (Multiply-Accumulate Engine with Reconfigurable interconnects) is investigated. Performance of a deep learning task is measured and compared in two different data flow strategies: NLR (No Local Reuse) and NVDLA (NVIDIA Deep Learning Accelerator), using an open source tool called MAESTRO (Modeling Accelerator Efficiency via Spatio-Temporal Resource Occupancy). Measured performance indicators of novel optimized architecture, NVDLA shows higher L1 and L2 computation reuse, and lower total runtime (cycles) in comparison to the other one.

scholarly journals Deep Learning Accelerators: A Case Study with MAESTRO

2020 ◽  
Author(s):  
Hamidreza Bolhasani ◽  
Somayyeh Jafarali Jassbi
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Performance Indicators ◽  
Learning Task ◽  
High Accuracy ◽  
Spatio Temporal ◽  
Almost All ◽  
L1 And L2 ◽  
Accuracy Speed

Abstract In recent years, deep learning has become one of the most important topics in computer sciences. Deep learning is a growing trend in the edge of technology and its applications are now seen in many aspects of our life such as object detection, speech recognition, natural language processing, etc. Currently, almost all major sciences and technologies are benefiting from the advantages of deep learning such as high accuracy, speed and flexibility. Therefore, any efforts in improving performance of related techniques is valuable. Deep learning accelerators are considered as hardware architecture, which are designed and optimized for increasing speed, efficiency and accuracy of computers that are running deep learning algorithms. In this paper, after reviewing some backgrounds on deep learning, a well-known accelerator architecture named MAERI (Multiply-Accumulate Engine with Reconfigurable interconnects) is investigated. Performance of a deep learning task is measured and compared in two different data flow strategies: NLR (No Local Reuse) and NVDLA (NVIDIA Deep Learning Accelerator), using an open source tool called MAESTRO (Modeling Accelerator Efficiency via Spatio-Temporal Resource Occupancy). Measured performance indicators of novel optimized architecture, NVDLA shows higher L1 and L2 computation reuse, and lower total runtime (cycles) in comparison to the other one.

scholarly journals Ontological Engineering For Source Code Generation

2020 ◽  
Vol 4 (2) ◽  
pp. 52-66
Author(s):  
Anas Hamid Alokla ◽  
◽  
Walaa Khaled Gad ◽  
Mustafa .M Aref ◽  
Abdel-badeeh .M Salem ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Code Generation ◽  
Source Code ◽  
Automatic Programming ◽  
Ontological Engineering ◽  
High Level ◽  
High Level Abstraction

Source Code Generation (SCG) is the sub-domain of the Automatic Programming (AP) that helps programmers to program using high-level abstraction. Recently, many researchers investigated many techniques to access SCG. The problem is to use the appropriate technique to generate the source code due to its purposes and the inputs. This paper introduces a review and an analysis related SCG techniques. Moreover, comparisons are presented for: techniques mapping, Natural Language Processing (NLP), knowledgebase, ontology, Specification Configuration Template (SCT) model and deep learning.

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