Computational Methods for Deep Learning
Theory, Algorithms, and Implementations
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30 Result(s)
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**wei Qi
English
Machine Learning
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Chapter and Conference Paper
Small Visual Object Detection in Smart Waste Classification Using Transformers with Deep Learning
Smart object waste classification is relatively essential for protecting the environment and saving resources. This is considered a vital pathway towards sustainability. In waste classification, we see that it...
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Chapter and Conference Paper
A Real-Time Kiwifruit Detection Based on Improved YOLOv7
In New Zealand (NZ), agriculture is an essential industry, Kiwifruits contribute significantly to the country’s overall exports. Traditionally Kiwifruits require manually picking up and heavily relies on human...
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Chapter and Conference Paper
Automatic Bleeding Risk Rating System of Gastric Varices
An automated bleeding risk rating system of gastric varices (GV) aims to predict the bleeding risk and severity of GV, in order to assist endoscopists in diagnosis and decrease the mortality rate of patients w...
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Chapter and Conference Paper
Vehicle-Related Distance Estimation Using Customized YOLOv7
With the popularity of autonomous driving, the development of ADAS (Advanced Driver Assistance Systems), especially collision avoidance systems, has become an important branch in the field of deep learning. In...
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Chapter
Transfer Learning and Ensemble Learning
In this chapter, we start from transfer learning and introduce the relationship between learners. We use ensemble learning to combine them together and hope to get a strong learner from a weak learner by chang...
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Chapter
Deep Learning Platforms
There are a plethora of deep learning platforms available at present. The famous one is MATLAB deep learning toolbox developed by MathWorks which simplifies deep learning computations and reduces the workload ...
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Chapter
Generative Adversarial Networks and Siamese Nets
In this chapter, we will emphasize computational iterations in GANs (i.e., generative adversarial networks) [46] and Siamese nets [3, 6, 15] . In deep learning, these models are named as contrastive networks [3]...
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Chapter
Manifold Learning and Graph Neural Network
In this chapter, we will introduce manifold learning and graph neural networks. We hope to introduce graphical probability models as the starting point of basestone. We need to introduce our readers why we sho...
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Book
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Book and Conference Proceedings
Image and Vision Computing
37th International Conference, IVCNZ 2022, Auckland, New Zealand, November 24–25, 2022, Revised Selected Papers
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Chapter
Introduction
This chapter covers the fundamentals of deep learning, therefore, we present relevant knowledge in chronological order so as to fully introduce the history of deep learning development; meanwhile, we review ho...
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Chapter
Convolutional Neural Networks and Recurrent Neural Networks
In this chapter, we will introduce the typical deep neural networks from the viewpoint of Convolutional Neural Network (CNN or ConvNet) family, especially , Single Shot MultiBox Detector (SSD) , and You Only...
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Chapter
Reinforcement Learning
In this chapter, we introduce fundamental concepts of reinforcement learning [21] such as , , deep Q- , and double Q- . We detail why reinforcement is thought as a method of deep learning.
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Chapter
Planning and Management of Charging Facilities for Electric Vehicle Sharing
Electric vehicle (EV) sharing has experienced rapid development and has served as a flexible and environmental friendly means for urban transportation. However, charging an EV sharing fleet is still a challeng...
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Book
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Chapter
CapsNet and Manifold Learning
is one of the relatively new methods in deep learning, which has taken topological of a scene into consideration. The output will be a vector to reflect this relationship. Meanwhile, manifold , which is em...
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Chapter
Transfer Learning and Ensemble Learning
In this chapter, we start from transfer learning and introduce the relationship between different learners; we use ensemble learning to combine them together and hope to get a strong learner from a weak learne...
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Chapter
Reinforcement Learning
In this chapter, we will introduce the fundamental concepts of reinforcement learning such as , , deep Q- , etc. We will introduce why reinforcement is thought as a method of deep learning. Then, mathemati...
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Chapter
Boltzmann Machines
In this chapter, we will introduce , restricted Boltzmann , and deep Boltzmann . We will generalize our deep neural networks from networks to general graphs, we will use probabilistic graphical to model t...
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Chapter
Deep Learning Platforms
There are many deep learning platforms available such as , , MXNet, , and Theano. Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, which originally was developed a...
